This document summarizes research on using a customized bacterial foraging optimization (CBFO) algorithm to solve job shop, flow shop, and open shop scheduling problems. CBFO combines bacterial foraging optimization (BFO) with ant colony optimization (ACO). The researchers tested CBFO on benchmark problem instances and randomly generated instances, finding it performed better than standard BFO. CBFO can effectively solve various shop scheduling problems and has potential for solving real-world scheduling issues.
Rice Data Simulator, Neural Network, Multiple linear regression, Prediction o...ijcseit
This paper is the continuation of the paper published by the authors Arun Balaji and Baskaran [2].
Multiple linear regression (MLR) equations were developed between the years of rice cultivation and Feed
Forward Back Propagation Neural Network (FFBPNN) method of predicted area of rice cultivation / rice
production for different districts pertaining to Kuruvai, Samba and Kodai seasons in Tamilnadu. The
average r
2
value in area of cultivation is 0.40 in Kuruvai season, 0.42 in Samba season and 0.46 in Kodai
season, where as the r2
value in rice production is 0.31 in Kuruvai season, 0.23 in Samba season and
0.42 in Kodai season. The Rice Data Simulator (RDS) predicted the area of rice cultivation and rice
production using the MLR equations developed in this research. The range of average predicted area for
Kuruvai, Samba and Kodai seasons varies from 12052.52 ha to 13595.32 ha, 48998.96 ha to 53324.54 ha
and 4241.23 ha to 6449.88 ha respectively whereas the range of average predicted rice production varies
from 45132.88 tonnes to 46074.48 tonnes in Kuruvai, 128619 tonnes to 139693.29 tonnes in Samba and
15446.07 to 20573.50 tonnes in Kodai seasons. The mean absolute relative error (ARE) between the
FFBPNN and multiple regression methods of prediction of area of rice cultivation was found to be 15.58%,
8.04% and 26.34% for the Kuruvai, Samba and the Kodai seasons respectively. The ARE for the rice
production was found to be 17%, 11.80% and 24.60% for the Kuruvai, Samba and the Kodai seasons
respectively. The paired t test between the FFBPNN and MLR methods of predicted area of cultivation in
Kuruvai shows that there is no significant difference between the two types of prediction for certain
districts.
MULTI PARENTS EXTENDED PRECEDENCE PRESERVATIVE CROSSOVER FOR JOB SHOP SCHEDUL...CHUNG SIN ONG
Job Shop Scheduling Problem (JSSP) is one of the hard combinatorial scheduling problems. This paper proposes a genetic algorithm with multi parents crossover called Extended Precedence Preservative Crossover (EPPX) that can be suitably modified and implemented with, in principal, unlimited number of parents which differ from conventional two parents crossover. JSSP representation encoded by using permutation with repetition guarantees the feasibility of chromosomes thus eliminates the legalization on children (offspring).The simulations are performed on a set of benchmark problems from the literatures and they indicate that the best solutions have the tendencies to be appeared by using 3-6 numbers of parents in the recombination. The comparison between the results of EPPX and other methodologies show the sustainability of multi parents recombination in producing competitive results to solve the JSSP.
In industries, the completion time of job problems in the manufacturing unit has risen significantly. In several types of current study, the job's completion time, or makespan, is reduced by taking straight paths, which is time-consuming. In this paper, we used an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm to solve this problem by precisely defining the fault occurrence location in order to rollback. We have used a short-term memory-based rollback recovery strategy to minimise the job's completion time by rolling back to its own short-term memory. The recent movements in Tabu quest are visited using short term memory. As compared to the ACO algorithm, our proposed ACOTS-Cmax solution is more efficient and takes less time to complete.
- Crossroads Deals website was launched to build SEO and capture leads from potential customers searching for deals online. Traffic is increasing over time as keyword rankings improve.
- The Targeter program uses the deals website to create marketing lists and advertise targeted offers to different audiences through multiple methods like mailers. Offers are rotated to continue engaging audiences.
- The Nurturing program pairs with Targeter in early phases and uses newsletters to establish trust and filter interested customers. Open and click rates are around industry standard and repetition is important to success. Regular reporting on metrics will be provided going forward.
Este documento é uma lista de medicamentos prescritos para um paciente, incluindo o nome do medicamento, dose, propósito, médico prescritor e datas de início e término do uso, bem como possíveis efeitos colaterais.
The document discusses the evolution of the internet from Web 1.0 to Web 3.0, highlighting how social media has transformed the web into a more interactive space where users generate and share content. It also examines how conversations online have become more natural and human as people use blogs, social networks, and other tools to connect, collaborate, and organize information through user-generated tags and reviews. Brands must now engage with customers through these new social channels as conversations shape markets.
This document summarizes key medical issues related to women's reproductive health. It discusses common breast, uterine, cervical, ovarian, and vaginal conditions as well as cancers that can affect these areas. It also covers pelvic inflammatory disease, endometriosis, hysterectomy, oophorectomy and other procedures. The document emphasizes the importance of screening and early detection for many of these conditions. It stresses gathering information from multiple sources to make informed health care decisions.
Rice Data Simulator, Neural Network, Multiple linear regression, Prediction o...ijcseit
This paper is the continuation of the paper published by the authors Arun Balaji and Baskaran [2].
Multiple linear regression (MLR) equations were developed between the years of rice cultivation and Feed
Forward Back Propagation Neural Network (FFBPNN) method of predicted area of rice cultivation / rice
production for different districts pertaining to Kuruvai, Samba and Kodai seasons in Tamilnadu. The
average r
2
value in area of cultivation is 0.40 in Kuruvai season, 0.42 in Samba season and 0.46 in Kodai
season, where as the r2
value in rice production is 0.31 in Kuruvai season, 0.23 in Samba season and
0.42 in Kodai season. The Rice Data Simulator (RDS) predicted the area of rice cultivation and rice
production using the MLR equations developed in this research. The range of average predicted area for
Kuruvai, Samba and Kodai seasons varies from 12052.52 ha to 13595.32 ha, 48998.96 ha to 53324.54 ha
and 4241.23 ha to 6449.88 ha respectively whereas the range of average predicted rice production varies
from 45132.88 tonnes to 46074.48 tonnes in Kuruvai, 128619 tonnes to 139693.29 tonnes in Samba and
15446.07 to 20573.50 tonnes in Kodai seasons. The mean absolute relative error (ARE) between the
FFBPNN and multiple regression methods of prediction of area of rice cultivation was found to be 15.58%,
8.04% and 26.34% for the Kuruvai, Samba and the Kodai seasons respectively. The ARE for the rice
production was found to be 17%, 11.80% and 24.60% for the Kuruvai, Samba and the Kodai seasons
respectively. The paired t test between the FFBPNN and MLR methods of predicted area of cultivation in
Kuruvai shows that there is no significant difference between the two types of prediction for certain
districts.
MULTI PARENTS EXTENDED PRECEDENCE PRESERVATIVE CROSSOVER FOR JOB SHOP SCHEDUL...CHUNG SIN ONG
Job Shop Scheduling Problem (JSSP) is one of the hard combinatorial scheduling problems. This paper proposes a genetic algorithm with multi parents crossover called Extended Precedence Preservative Crossover (EPPX) that can be suitably modified and implemented with, in principal, unlimited number of parents which differ from conventional two parents crossover. JSSP representation encoded by using permutation with repetition guarantees the feasibility of chromosomes thus eliminates the legalization on children (offspring).The simulations are performed on a set of benchmark problems from the literatures and they indicate that the best solutions have the tendencies to be appeared by using 3-6 numbers of parents in the recombination. The comparison between the results of EPPX and other methodologies show the sustainability of multi parents recombination in producing competitive results to solve the JSSP.
In industries, the completion time of job problems in the manufacturing unit has risen significantly. In several types of current study, the job's completion time, or makespan, is reduced by taking straight paths, which is time-consuming. In this paper, we used an Improved Ant Colony Optimization and Tabu Search (ACOTS) algorithm to solve this problem by precisely defining the fault occurrence location in order to rollback. We have used a short-term memory-based rollback recovery strategy to minimise the job's completion time by rolling back to its own short-term memory. The recent movements in Tabu quest are visited using short term memory. As compared to the ACO algorithm, our proposed ACOTS-Cmax solution is more efficient and takes less time to complete.
- Crossroads Deals website was launched to build SEO and capture leads from potential customers searching for deals online. Traffic is increasing over time as keyword rankings improve.
- The Targeter program uses the deals website to create marketing lists and advertise targeted offers to different audiences through multiple methods like mailers. Offers are rotated to continue engaging audiences.
- The Nurturing program pairs with Targeter in early phases and uses newsletters to establish trust and filter interested customers. Open and click rates are around industry standard and repetition is important to success. Regular reporting on metrics will be provided going forward.
Este documento é uma lista de medicamentos prescritos para um paciente, incluindo o nome do medicamento, dose, propósito, médico prescritor e datas de início e término do uso, bem como possíveis efeitos colaterais.
The document discusses the evolution of the internet from Web 1.0 to Web 3.0, highlighting how social media has transformed the web into a more interactive space where users generate and share content. It also examines how conversations online have become more natural and human as people use blogs, social networks, and other tools to connect, collaborate, and organize information through user-generated tags and reviews. Brands must now engage with customers through these new social channels as conversations shape markets.
This document summarizes key medical issues related to women's reproductive health. It discusses common breast, uterine, cervical, ovarian, and vaginal conditions as well as cancers that can affect these areas. It also covers pelvic inflammatory disease, endometriosis, hysterectomy, oophorectomy and other procedures. The document emphasizes the importance of screening and early detection for many of these conditions. It stresses gathering information from multiple sources to make informed health care decisions.
A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problemsijpla
Bio-Inspired computing is the subset of Nature-Inspired computing. Job Shop Scheduling Problem is
categorized under popular scheduling problems. In this research work, Bacterial Foraging Optimization
was hybridized with Ant Colony Optimization and a new technique Hybrid Bacterial Foraging
Optimization for solving Job Shop Scheduling Problem was proposed. The optimal solutions obtained by
proposed Hybrid Bacterial Foraging Optimization algorithms are much better when compared with the
solutions obtained by Bacterial Foraging Optimization algorithm for well-known test problems of different
sizes. From the implementation of this research work, it could be observed that the proposed Hybrid
Bacterial Foraging Optimization was effective than Bacterial Foraging Optimization algorithm in solving
Job Shop Scheduling Problems. Hybrid Bacterial Foraging Optimization is used to implement real world
Job Shop Scheduling Problems
In this paper, a modified invasive weed optimization (IWO) algorithm is presented for
optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria
to minimize the maximum completion time (makespan), the total workload of machines and the
workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the
ecological behaviour of weeds in colonizing and finding suitable place for growth and
reproduction. IWO is developed to solve continuous optimization problems that’s why the
heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position
values to the discrete job sequences. The computational experiments show that the proposed
algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to
find the optimal and best-known solutions on the instances studied.
AN ANT COLONY OPTIMIZATION ALGORITHM FOR JOB SHOP SCHEDULING PROBLEMijaia
This document summarizes an ant colony optimization algorithm for solving job shop scheduling problems. It describes how ant colony optimization is inspired by the behavior of real ants finding shortest paths between their nest and food sources. The algorithm models artificial ants probabilistically constructing solutions to the job shop scheduling problem. The ants are guided by pheromone trails and heuristic information associated with edges in a graph representation of the problem. The pheromone trails, representing learned desirability of choices, are updated based on the quality of the solutions constructed by the ants. The algorithm aims to find high-quality solutions with relatively few evaluations of the objective function for minimizing makespan in job shop scheduling problems.
This paper examines using a shifting bottleneck heuristic algorithm to minimize the makespan of a job shop production system at Dejena Aviation Industry in Ethiopia. The authors collected secondary data on five machines processing five jobs. Applying the shifting bottleneck algorithm resulted in an 8.33% reduction in total makespan. Machine one (41%) and three (36%) were the least utilized, while machine three (64%) and five (59%) were the busiest. The paper reviews literature on job shop scheduling and shifting bottleneck approaches, provides background on modeling job shops, and describes the shifting bottleneck algorithm used in the study.
This document summarizes a research paper that investigates minimizing the makespan of a job shop production system at Dejena Aviation Industry using a shifting bottleneck heuristic algorithm. The paper analyzes production data from 5 machines and 5 jobs. It finds that applying the shifting bottleneck algorithm reduces the total makespan by 8.33%. Machine 1 and 3 are the least utilized at 41% and 36% respectively, while machine 3 and 5 are the busiest at 64% and 59%. The algorithm iteratively identifies the bottleneck machine and reschedules jobs to minimize the maximum lateness time until an optimal solution is reached.
This document summarizes a research paper that investigates minimizing the makespan of a job shop production system at Dejena Aviation Industry using a shifting bottleneck heuristic algorithm. The paper analyzes production data from 5 machines and 5 jobs. It finds that applying the shifting bottleneck algorithm reduces the total makespan by 8.33%. Machine 1 and 3 are the least utilized at 41% and 36% respectively, while machine 3 and 5 are the busiest at 64% and 59%. The algorithm iteratively identifies bottleneck machines and reschedules jobs to minimize the maximum lateness time and reduce the makespan.
This document summarizes a research paper that investigates minimizing the makespan of a job shop production system at Dejena Aviation Industry using a shifting bottleneck heuristic algorithm. The paper analyzes production data from 5 machines and 5 jobs. It finds that applying the shifting bottleneck algorithm reduces the total makespan by 8.33%. Machine 1 and 3 are the least utilized at 41% and 36% respectively, while machine 3 and 5 are the busiest at 64% and 59%. The algorithm iteratively identifies bottleneck machines and reschedules jobs to minimize the maximum lateness time and reduce the makespan.
Solving Multi-level, Multi-product and Multi-period Lot Sizing and Scheduling...Editor IJCATR
In this paper, a new model of capacitated lot sizing and scheduling in a permutation flow shop is developed. In this model
demand can be totally backlogged. Setups can be carryover and are sequence-dependent. It is well-known from literatures that
capacitated lot sizing problem in permutation flow shop systems are NP-hard. This means the model is solved in polynomial time and
metaheuristics algorithms are capable of solving these problems within reasonable computing load. Metaheuristic algorithms find more
applications in recent researches. On this concern this paper proposes two evolutionary algorithms, one of the most popular namely,
Genetic Algorithm (GA) and one of the most powerful population base algorithms namely, Imperialist Competitive Algorithm (ICA).
The proposed algorithms are calibrate by Taguchi method and be compared against a presented lower bound. Some numerical
examples are solved by both the algorithms and the lower bound. The quality of solution obtained by the proposed algorithm showed
superiority of ICA to GA.
This paper considers a flow shop scheduling problem. The flow shop scheduling is
characterized “n” jobs and “m” machines in series with unidirectional flow of work with
variety of jobs being processed sequentially in a single pass manner. Most real world
problems are NP-hard in nature. The essential complexity of the problem necessitates the
use of meta-heuristics for solving flow shop scheduling problem. The paper addresses the
flow shop scheduling problem to minimize the makespan time with specific batch size
using Particle Swarm Optimization (PSO) and comparing the results with an real time
company production sequence.
The document proposes a hybrid algorithm combining genetic algorithm and cuckoo search optimization to solve job shop scheduling problems. It aims to minimize makespan (completion time of all jobs) by scheduling jobs on machines. The genetic algorithm is used to explore the search space but can get trapped in local optima. Cuckoo search optimization performs local search faster than genetic algorithm and helps avoid local optima. Experimental results on benchmark problems show the hybrid algorithm yields better solutions in terms of makespan and runtime compared to genetic algorithm and ant colony optimization algorithms.
Multiobjective Flexible Job Shop Scheduling Using A Modified Invasive Weed Op...ijsc
This document summarizes a research paper that proposes using a modified Invasive Weed Optimization (IWO) algorithm to solve multi-objective flexible job shop scheduling problems. The goals are to minimize makespan, total machine workload, and workload of the most loaded machine. IWO is a bio-inspired metaheuristic algorithm that mimics how weeds colonize and spread. The researchers encode job scheduling solutions as "weeds" and use properties of weed colonization like reproduction, spatial dispersal, and competition in the IWO algorithm. Computational tests on benchmark problems show the modified IWO finds optimal or best-known solutions, showing it is competitive with state-of-the-art methods for flexible job shop scheduling.
Recently, many studies are carried out with inspirations from ecological phenomena for developing
optimization techniques. The new algorithm that is motivated by a common phenomenon in agriculture is
colonization of invasive weeds. In this paper, a modified invasive weed optimization (IWO) algorithm is
presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the
criteria to minimize the maximum completion time (makespan), the total workload of machines and the
workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour
of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve
continuous optimization problems that’s why the heuristic rule the Smallest Position Value (SPV) is used to
convert the continuous position values to the discrete job sequences. The computational experiments show
that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is
able to find the optimal and best-known solutions on the instances studied.
Discrete penguins search optimization algorithm to solve flow shop schedulin...IJECEIAES
Flow shop scheduling problem is one of the most classical NP-hard optimization problem. Which aims to find the best planning that minimizes the makespan (total completion time) of a set of tasks in a set of machines with certain constraints. In this paper, we propose a new nature inspired metaheuristic to solve the flow shop scheduling problem (FSSP), called penguins search optimization algorithm (PeSOA) based on collaborative hunting strategy of penguins.The operators and parameter values of PeSOA redefined to solve this problem. The performance of the penguins search optimization algorithm is tested on a set of benchmarks instances of FSSP from OR-Library, The results of the tests show that PeSOA is superior to some other metaheuristics algorithms, in terms of the quality of the solutions found and the execution time.
In some industries as foundries, it is not technically feasible to interrupt a processor between jobs. This restriction gives rise to a scheduling problem called no-idle scheduling. This paper deals with scheduling of no-idle open shops to minimize maximum completion time of jobs, called makespan. The problem is first mathematically formulated by three different mixed integer linear programming models. Since open shop scheduling problems are NP-hard, only small instances can be solved to optimality using these models. Thus, to solve large instances, two meta-heuristics based on simulated annealing and genetic algorithms are developed. A complete numerical experiment is conducted and the developed models and algorithms are compared. The results show that genetic algorithm outperforms simulated annealing.
Comparative Analysis of Metaheuristic Approaches for Makespan Minimization fo...IJECEIAES
This paper provides comparative analysis of various metaheuristic approaches for m-machine no wait flow shop scheduling (NWFSS) problem with makespan as an optimality criterion. NWFSS problem is NP hard and brute force method unable to find the solutions so approximate solutions are found with metaheuristic algorithms. The objective is to find out the scheduling sequence of jobs to minimize total completion time. In order to meet the objective criterion, existing metaheuristic techniques viz. Tabu Search (TS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are implemented for small and large sized problems and effectiveness of these techniques are measured with statistical metric.
This document discusses using a genetic algorithm to solve the job shop scheduling problem. It begins with an abstract that introduces using genetic algorithms for job shop scheduling. It then provides more details on the problem and discusses using genetic algorithms with modifications like generating the initial population using priority rules and incorporating critical block and disjunctive graph distance in the crossover and mutation operations. The document outlines the genetic algorithm approach with sections on chromosome representation and decoding, data structures, generating the initial population, crossover and mutation operations. The goal is to minimize the makespan value for job shop scheduling.
A MULTI-POPULATION BASED FROG-MEMETIC ALGORITHM FOR JOB SHOP SCHEDULING PROBLEMacijjournal
The Job Shop Scheduling Problem (JSSP) is a well known practical planning problem in the
manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this
paper, we develop a three-stage hybrid approach called JSFMA to solve the JSSP. In JSFMA,
considering a method similar to Shuffled Frog Leaping algorithm we divide the population in several sub
populations and then solve the problem using a Memetic algorithm. The proposed approach have been
compared with other algorithms for the Job Shop Scheduling and evaluated with satisfactory results on a
set of the JSSP instances derived from classical Job Shop Scheduling benchmarks. We have solved 20
benchmark problems from Lawrence’s datasets and compared the results obtained with the results of the
algorithms established in the literature. The experimental results show that JSFMA could gain the best
known makespan in 17 out of 20 problems.
A Hybrid Pareto Based Multi Objective Evolutionary Algorithm for a Partial Fl...IOSRJM
In this paper, A Partial flexible, open-shop scheduling problem (FOSP) is a combinatorial optimization problem. This work, with the objective of optimizing the makespan of an FOSP uses a hybrid Pareto based optimization (HPBO) approach. The problems are tested on Taillard’s benchmark problems. The consequences of Nawaz, Encore and Ham (NEH) meta heuristic are introduced to the HPBO to direct the search into a quality space. Variable neighbourhood search for (VNS) is employed to overcome the early convergence of the HPBO and helps in global search. The results are compared with standalone HPBO, traditional meta heuristics and the Taillard’s upper bounds. Five problem locate are taken from Taillard’s benchmark problems and are solved for various problem sizes. Thus a total of 35 problems is given to explain. The experimental results show that the solution quality of FOSP can be improved if the search is directed in a quality space in light of the proposed LHPBO approach (LHPBO-NEH-VNS).
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...inventionjournals
In this paper, we propose a hybrid genetic algorithm to solve assembly line balancing problem. We put into the optimization framework of maximizing assembly line efficiency and minimizing total idle time simultaneously. The model is able to deal with more realistic situation of assembly line balancing problem such as zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively, so we aim to provide its exploring capability by sequentially hybridizing the well-known assignment rules heuristics with genetic algorithm.
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...inventionjournals
In this paper, we propose a hybrid genetic algorithm to solve assembly line balancing problem. We put into the optimization framework of maximizing assembly line efficiency and minimizing total idle time simultaneously. The model is able to deal with more realistic situation of assembly line balancing problem such as zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively, so we aim to provide its exploring capability by sequentially hybridizing the well-known assignment rules heuristics with genetic algorithm
A CONCEPTUAL FRAMEWORK OF A DETECTIVE MODEL FOR SOCIAL BOT CLASSIFICATIONijasa
Social media platform has greatly enhanced human interactive activities in the virtual community. Virtual
socialization has positively influenced social bonding among social media users irrespective of one’s
location in the connected global village. Human user and social bot user are the two types of social media
users. While human users personally operate their social media accounts, social bot users are developed
software that manages a social media account for the human user called the botmaster. This botmaster in
most cases are hackers with bad intention of attacking social media users through various attacking mode
using social bots. The aim of this research work is to design an intelligent framework that will prevent
attacks through social bots on social media network platforms.
DESIGN OF A MINIATURE RECTANGULAR PATCH ANTENNA FOR KU BAND APPLICATIONSijasa
A significant portion of communication devices employs microstrip antennas because of their compact size,
low profile, and ability to conform to both planar and non-planar surfaces. To achieve this, we present a
miniature inset-fed rectangular patch antenna using partial ground plane for Ku band applications. The
proposed antenna design used an operating frequency of 15.5 GHz, a FR4 substrate with a dielectric
constant of 4.3, and a thickness of 1.4 mm. It is fed by a 50 Ω inset feedline. Computer simulation
technology (CST) software is used to design, simulate, and analyze. The simulation yields the antenna
performance parameters, including return loss (S11), bandwidth, VSWR, gain, directivity, and radiation
efficiency. The simulation findings revealed that the proposed antenna resonated at 15.5 GHz, with a
return loss of -22.312 dB, a bandwidth of 2.73 GHz (2730 MHz), VSWR of 1.17, a gain of 3.843 dBi, a
directivity of 5.926 dBi, and an antenna efficiency of -2.083 dB (61.901%).
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A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problemsijpla
Bio-Inspired computing is the subset of Nature-Inspired computing. Job Shop Scheduling Problem is
categorized under popular scheduling problems. In this research work, Bacterial Foraging Optimization
was hybridized with Ant Colony Optimization and a new technique Hybrid Bacterial Foraging
Optimization for solving Job Shop Scheduling Problem was proposed. The optimal solutions obtained by
proposed Hybrid Bacterial Foraging Optimization algorithms are much better when compared with the
solutions obtained by Bacterial Foraging Optimization algorithm for well-known test problems of different
sizes. From the implementation of this research work, it could be observed that the proposed Hybrid
Bacterial Foraging Optimization was effective than Bacterial Foraging Optimization algorithm in solving
Job Shop Scheduling Problems. Hybrid Bacterial Foraging Optimization is used to implement real world
Job Shop Scheduling Problems
In this paper, a modified invasive weed optimization (IWO) algorithm is presented for
optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria
to minimize the maximum completion time (makespan), the total workload of machines and the
workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the
ecological behaviour of weeds in colonizing and finding suitable place for growth and
reproduction. IWO is developed to solve continuous optimization problems that’s why the
heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position
values to the discrete job sequences. The computational experiments show that the proposed
algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to
find the optimal and best-known solutions on the instances studied.
AN ANT COLONY OPTIMIZATION ALGORITHM FOR JOB SHOP SCHEDULING PROBLEMijaia
This document summarizes an ant colony optimization algorithm for solving job shop scheduling problems. It describes how ant colony optimization is inspired by the behavior of real ants finding shortest paths between their nest and food sources. The algorithm models artificial ants probabilistically constructing solutions to the job shop scheduling problem. The ants are guided by pheromone trails and heuristic information associated with edges in a graph representation of the problem. The pheromone trails, representing learned desirability of choices, are updated based on the quality of the solutions constructed by the ants. The algorithm aims to find high-quality solutions with relatively few evaluations of the objective function for minimizing makespan in job shop scheduling problems.
This paper examines using a shifting bottleneck heuristic algorithm to minimize the makespan of a job shop production system at Dejena Aviation Industry in Ethiopia. The authors collected secondary data on five machines processing five jobs. Applying the shifting bottleneck algorithm resulted in an 8.33% reduction in total makespan. Machine one (41%) and three (36%) were the least utilized, while machine three (64%) and five (59%) were the busiest. The paper reviews literature on job shop scheduling and shifting bottleneck approaches, provides background on modeling job shops, and describes the shifting bottleneck algorithm used in the study.
This document summarizes a research paper that investigates minimizing the makespan of a job shop production system at Dejena Aviation Industry using a shifting bottleneck heuristic algorithm. The paper analyzes production data from 5 machines and 5 jobs. It finds that applying the shifting bottleneck algorithm reduces the total makespan by 8.33%. Machine 1 and 3 are the least utilized at 41% and 36% respectively, while machine 3 and 5 are the busiest at 64% and 59%. The algorithm iteratively identifies the bottleneck machine and reschedules jobs to minimize the maximum lateness time until an optimal solution is reached.
This document summarizes a research paper that investigates minimizing the makespan of a job shop production system at Dejena Aviation Industry using a shifting bottleneck heuristic algorithm. The paper analyzes production data from 5 machines and 5 jobs. It finds that applying the shifting bottleneck algorithm reduces the total makespan by 8.33%. Machine 1 and 3 are the least utilized at 41% and 36% respectively, while machine 3 and 5 are the busiest at 64% and 59%. The algorithm iteratively identifies bottleneck machines and reschedules jobs to minimize the maximum lateness time and reduce the makespan.
This document summarizes a research paper that investigates minimizing the makespan of a job shop production system at Dejena Aviation Industry using a shifting bottleneck heuristic algorithm. The paper analyzes production data from 5 machines and 5 jobs. It finds that applying the shifting bottleneck algorithm reduces the total makespan by 8.33%. Machine 1 and 3 are the least utilized at 41% and 36% respectively, while machine 3 and 5 are the busiest at 64% and 59%. The algorithm iteratively identifies bottleneck machines and reschedules jobs to minimize the maximum lateness time and reduce the makespan.
Solving Multi-level, Multi-product and Multi-period Lot Sizing and Scheduling...Editor IJCATR
In this paper, a new model of capacitated lot sizing and scheduling in a permutation flow shop is developed. In this model
demand can be totally backlogged. Setups can be carryover and are sequence-dependent. It is well-known from literatures that
capacitated lot sizing problem in permutation flow shop systems are NP-hard. This means the model is solved in polynomial time and
metaheuristics algorithms are capable of solving these problems within reasonable computing load. Metaheuristic algorithms find more
applications in recent researches. On this concern this paper proposes two evolutionary algorithms, one of the most popular namely,
Genetic Algorithm (GA) and one of the most powerful population base algorithms namely, Imperialist Competitive Algorithm (ICA).
The proposed algorithms are calibrate by Taguchi method and be compared against a presented lower bound. Some numerical
examples are solved by both the algorithms and the lower bound. The quality of solution obtained by the proposed algorithm showed
superiority of ICA to GA.
This paper considers a flow shop scheduling problem. The flow shop scheduling is
characterized “n” jobs and “m” machines in series with unidirectional flow of work with
variety of jobs being processed sequentially in a single pass manner. Most real world
problems are NP-hard in nature. The essential complexity of the problem necessitates the
use of meta-heuristics for solving flow shop scheduling problem. The paper addresses the
flow shop scheduling problem to minimize the makespan time with specific batch size
using Particle Swarm Optimization (PSO) and comparing the results with an real time
company production sequence.
The document proposes a hybrid algorithm combining genetic algorithm and cuckoo search optimization to solve job shop scheduling problems. It aims to minimize makespan (completion time of all jobs) by scheduling jobs on machines. The genetic algorithm is used to explore the search space but can get trapped in local optima. Cuckoo search optimization performs local search faster than genetic algorithm and helps avoid local optima. Experimental results on benchmark problems show the hybrid algorithm yields better solutions in terms of makespan and runtime compared to genetic algorithm and ant colony optimization algorithms.
Multiobjective Flexible Job Shop Scheduling Using A Modified Invasive Weed Op...ijsc
This document summarizes a research paper that proposes using a modified Invasive Weed Optimization (IWO) algorithm to solve multi-objective flexible job shop scheduling problems. The goals are to minimize makespan, total machine workload, and workload of the most loaded machine. IWO is a bio-inspired metaheuristic algorithm that mimics how weeds colonize and spread. The researchers encode job scheduling solutions as "weeds" and use properties of weed colonization like reproduction, spatial dispersal, and competition in the IWO algorithm. Computational tests on benchmark problems show the modified IWO finds optimal or best-known solutions, showing it is competitive with state-of-the-art methods for flexible job shop scheduling.
Recently, many studies are carried out with inspirations from ecological phenomena for developing
optimization techniques. The new algorithm that is motivated by a common phenomenon in agriculture is
colonization of invasive weeds. In this paper, a modified invasive weed optimization (IWO) algorithm is
presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the
criteria to minimize the maximum completion time (makespan), the total workload of machines and the
workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour
of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve
continuous optimization problems that’s why the heuristic rule the Smallest Position Value (SPV) is used to
convert the continuous position values to the discrete job sequences. The computational experiments show
that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is
able to find the optimal and best-known solutions on the instances studied.
Discrete penguins search optimization algorithm to solve flow shop schedulin...IJECEIAES
Flow shop scheduling problem is one of the most classical NP-hard optimization problem. Which aims to find the best planning that minimizes the makespan (total completion time) of a set of tasks in a set of machines with certain constraints. In this paper, we propose a new nature inspired metaheuristic to solve the flow shop scheduling problem (FSSP), called penguins search optimization algorithm (PeSOA) based on collaborative hunting strategy of penguins.The operators and parameter values of PeSOA redefined to solve this problem. The performance of the penguins search optimization algorithm is tested on a set of benchmarks instances of FSSP from OR-Library, The results of the tests show that PeSOA is superior to some other metaheuristics algorithms, in terms of the quality of the solutions found and the execution time.
In some industries as foundries, it is not technically feasible to interrupt a processor between jobs. This restriction gives rise to a scheduling problem called no-idle scheduling. This paper deals with scheduling of no-idle open shops to minimize maximum completion time of jobs, called makespan. The problem is first mathematically formulated by three different mixed integer linear programming models. Since open shop scheduling problems are NP-hard, only small instances can be solved to optimality using these models. Thus, to solve large instances, two meta-heuristics based on simulated annealing and genetic algorithms are developed. A complete numerical experiment is conducted and the developed models and algorithms are compared. The results show that genetic algorithm outperforms simulated annealing.
Comparative Analysis of Metaheuristic Approaches for Makespan Minimization fo...IJECEIAES
This paper provides comparative analysis of various metaheuristic approaches for m-machine no wait flow shop scheduling (NWFSS) problem with makespan as an optimality criterion. NWFSS problem is NP hard and brute force method unable to find the solutions so approximate solutions are found with metaheuristic algorithms. The objective is to find out the scheduling sequence of jobs to minimize total completion time. In order to meet the objective criterion, existing metaheuristic techniques viz. Tabu Search (TS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are implemented for small and large sized problems and effectiveness of these techniques are measured with statistical metric.
This document discusses using a genetic algorithm to solve the job shop scheduling problem. It begins with an abstract that introduces using genetic algorithms for job shop scheduling. It then provides more details on the problem and discusses using genetic algorithms with modifications like generating the initial population using priority rules and incorporating critical block and disjunctive graph distance in the crossover and mutation operations. The document outlines the genetic algorithm approach with sections on chromosome representation and decoding, data structures, generating the initial population, crossover and mutation operations. The goal is to minimize the makespan value for job shop scheduling.
A MULTI-POPULATION BASED FROG-MEMETIC ALGORITHM FOR JOB SHOP SCHEDULING PROBLEMacijjournal
The Job Shop Scheduling Problem (JSSP) is a well known practical planning problem in the
manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this
paper, we develop a three-stage hybrid approach called JSFMA to solve the JSSP. In JSFMA,
considering a method similar to Shuffled Frog Leaping algorithm we divide the population in several sub
populations and then solve the problem using a Memetic algorithm. The proposed approach have been
compared with other algorithms for the Job Shop Scheduling and evaluated with satisfactory results on a
set of the JSSP instances derived from classical Job Shop Scheduling benchmarks. We have solved 20
benchmark problems from Lawrence’s datasets and compared the results obtained with the results of the
algorithms established in the literature. The experimental results show that JSFMA could gain the best
known makespan in 17 out of 20 problems.
A Hybrid Pareto Based Multi Objective Evolutionary Algorithm for a Partial Fl...IOSRJM
In this paper, A Partial flexible, open-shop scheduling problem (FOSP) is a combinatorial optimization problem. This work, with the objective of optimizing the makespan of an FOSP uses a hybrid Pareto based optimization (HPBO) approach. The problems are tested on Taillard’s benchmark problems. The consequences of Nawaz, Encore and Ham (NEH) meta heuristic are introduced to the HPBO to direct the search into a quality space. Variable neighbourhood search for (VNS) is employed to overcome the early convergence of the HPBO and helps in global search. The results are compared with standalone HPBO, traditional meta heuristics and the Taillard’s upper bounds. Five problem locate are taken from Taillard’s benchmark problems and are solved for various problem sizes. Thus a total of 35 problems is given to explain. The experimental results show that the solution quality of FOSP can be improved if the search is directed in a quality space in light of the proposed LHPBO approach (LHPBO-NEH-VNS).
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...inventionjournals
In this paper, we propose a hybrid genetic algorithm to solve assembly line balancing problem. We put into the optimization framework of maximizing assembly line efficiency and minimizing total idle time simultaneously. The model is able to deal with more realistic situation of assembly line balancing problem such as zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively, so we aim to provide its exploring capability by sequentially hybridizing the well-known assignment rules heuristics with genetic algorithm.
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...inventionjournals
In this paper, we propose a hybrid genetic algorithm to solve assembly line balancing problem. We put into the optimization framework of maximizing assembly line efficiency and minimizing total idle time simultaneously. The model is able to deal with more realistic situation of assembly line balancing problem such as zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively, so we aim to provide its exploring capability by sequentially hybridizing the well-known assignment rules heuristics with genetic algorithm
Similar to An Enhanced Bio-Stimulated Methodology to Resolve Shop Scheduling Problems (20)
A CONCEPTUAL FRAMEWORK OF A DETECTIVE MODEL FOR SOCIAL BOT CLASSIFICATIONijasa
Social media platform has greatly enhanced human interactive activities in the virtual community. Virtual
socialization has positively influenced social bonding among social media users irrespective of one’s
location in the connected global village. Human user and social bot user are the two types of social media
users. While human users personally operate their social media accounts, social bot users are developed
software that manages a social media account for the human user called the botmaster. This botmaster in
most cases are hackers with bad intention of attacking social media users through various attacking mode
using social bots. The aim of this research work is to design an intelligent framework that will prevent
attacks through social bots on social media network platforms.
DESIGN OF A MINIATURE RECTANGULAR PATCH ANTENNA FOR KU BAND APPLICATIONSijasa
A significant portion of communication devices employs microstrip antennas because of their compact size,
low profile, and ability to conform to both planar and non-planar surfaces. To achieve this, we present a
miniature inset-fed rectangular patch antenna using partial ground plane for Ku band applications. The
proposed antenna design used an operating frequency of 15.5 GHz, a FR4 substrate with a dielectric
constant of 4.3, and a thickness of 1.4 mm. It is fed by a 50 Ω inset feedline. Computer simulation
technology (CST) software is used to design, simulate, and analyze. The simulation yields the antenna
performance parameters, including return loss (S11), bandwidth, VSWR, gain, directivity, and radiation
efficiency. The simulation findings revealed that the proposed antenna resonated at 15.5 GHz, with a
return loss of -22.312 dB, a bandwidth of 2.73 GHz (2730 MHz), VSWR of 1.17, a gain of 3.843 dBi, a
directivity of 5.926 dBi, and an antenna efficiency of -2.083 dB (61.901%).
SMART SOUND SYSTEM APPLIED FOR THE EXTENSIVE CARE OF PEOPLE WITH HEARING IMPA...ijasa
We, as normal people, have access to a potent communication tool, which is sound. Although we can continuously gather, analyse, and interpret sounds thanks to our sense of hearing, it can be challenging for people with hearing impairment to perceive their surroundings through sound. Also known as PWHI (People with Hearing Impairment). Auditory/phonic impairment is one of the most prevailing sensory deficits in humans at present. Fortunately, there is room to apply a solution to this issue, given the development of technology. Our project involves capturing ambient sounds from the user’s surroundings and notifying the user through a mobile application using IoT and Deep Learning. Its architecture offers sound recognition using a tool, such as a microphone, to capture sounds from the user's surroundings. These sounds are identified and categorized as ambient sounds, like a doorbell, baby cry, and dog barking; as well as emergency-related sounds, such as alarms, sirens, et
AN INTELLIGENT AND DATA-DRIVEN MOBILE VOLUNTEER EVENT MANAGEMENT PLATFORM USI...ijasa
In Lewis and Clark High School’s Key Club, meetings are always held in a crowded classroom. The
system of event sign-up is inefficient and hinders members from joining events. This has led to students
becoming discouraged from joining Key Club and often resulted in a lack of volunteers for important
events. The club needed a more efficient way of connecting volunteers with volunteering opportunities. To
solve this problem, we developed a VolunteerMatch Mobile application using Dart and Flutter framework
for Key Club to use. The next steps will be to add a volunteer event recommendation and matching feature,
utilizing the results from the research on machine learning models and algorithms in this paper.
A STUDY OF IOT BASED REAL-TIME SOLAR POWER REMOTE MONITORING SYSTEMijasa
We have Developed an IoT-based real-time solar power monitoring system in this paper. It seeks an opensource IoT solution that can collect real-time data and continuously monitor the power output and environmental conditions of a photovoltaic panel.The Objective of this work is to continuously monitor the status of various parameters associated with solar systems through sensors without visiting manually, saving time and ensures efficient power output from PV panels while monitoring for faulty solar panels, weather conditionsand other such issues that affect solar effectiveness.Manually, the user must use a multimeter to determine what value of measurement of the system is appropriate for appliance consumers, which is difficult for the larger System. But the Solar Energy Monitoring system is designed to make it easier for users to use the solar system.This system is comprised of a microcontroller (Node MCU), a PV panel, sensors (INA219 Current Module, Digital Temperature Sensor, LDR), a Battery Charger Module, and a battery. The data from the PV panels and other appliances are sent to the cloud (Thingspeak) via the internet using IoT technology and a Wi-Fi module (NodeMCU). It also allows users in remote areas to monitor the parameters of the solar power plant using connected devices. The user can view the current, previous, and average parameters of the solar PV system, such as voltage, current, temperature, and light intensity using a Graphical User Interface. This will facilitate fault detection and maintenance of the solar power plant easier and saves time.
SENSOR BASED SMART IRRIGATION SYSTEM WITH MONITORING AND CONTROLLING USING IN...ijasa
This document presents a sensor-based smart irrigation system using IoT. The system uses soil moisture, temperature, and humidity sensors connected to a NodeMCU microcontroller. The sensor data is sent to a cloud server (ThingSpeak) and displayed as graphs on a website. A web page allows users to control a water pump remotely. The system was tested on a field over one day, recording sensor data and pump status in the morning, afternoon and night. Test results showed the pump turned on when soil moisture fell below a threshold and off when above a threshold, conserving water. The smart irrigation system allows remote monitoring and control to help farmers irrigate crops efficiently with minimal human effort or water waste.
COMPARISON OF BIT ERROR RATE PERFORMANCE OF VARIOUS DIGITAL MODULATION SCHEME...ijasa
This document compares the bit error rate (BER) performance of different digital modulation schemes (BPSK, QPSK, 16-QAM) over AWGN and Rayleigh fading channels using Simulink simulations. It finds that BPSK outperforms QPSK and 16-QAM in both channels. The BER is evaluated for these modulation schemes using two equalization techniques: constant modulus algorithm (CMA) and maximum likelihood sequence estimation (MLSE). According to the results, BPSK has better BER performance than QPSK and 16-QAM when using either equalizer, especially at lower SNR values. CMA equalization works better than MLSE equalization for all modulation schemes based on the BER values obtained.
PERFORMANCE OF CONVOLUTION AND CRC CHANNEL ENCODED V-BLAST 4×4 MIMO MCCDMA WI...ijasa
This document analyzes the performance of a 4x4 Vertical Bell Labs Layered Space-Time (V-Blast) multiple-input multiple-output multi-carrier code division multiple access (MIMO MC-CDMA) wireless communication system using different digital modulation schemes. The system uses minimum mean square error (MMSE) signal detection and 1/2-rated convolution and cyclic redundancy check (CRC) channel encoding. Simulation results show that binary phase-shift keying (BPSK) modulation outperforms differential phase-shift keying (DPSK), quadrature phase-shift keying (QPSK), and 16-quadrature amplitude modulation (QAM), achieving the lowest bit error rate (BER) especially at
A SCRUTINY TO ATTACK ISSUES AND SECURITY CHALLENGES IN CLOUD COMPUTINGijasa
Cloud computing is an anthology in which one or more computers are connected in a network. Cloud
computing is a cluster of lattice computing, autonomic computing and utility computing. Cloud provides an
on demand services to the users. Many numbers of users access the cloud to utilize the cloud resources.
The security is one the major problem in cloud computing. Hence security is a major issue in cloud
computing. Providing security is a major requirement of cloud computing. The study enclose all the
security issues and attack issues in cloud computing.
The International Journal of Ambient Systems and Applications (IJASA) ijasa
The International Journal of Ambient Systems and applications is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of ambient Systems. The journal focuses on all technical and practical aspects of ambient Systems, networks, technologies and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced ambient Systems and establishing new collaborations in these areas.Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in ambient Systems.
A SCRUTINY TO ATTACK ISSUES AND SECURITY CHALLENGES IN CLOUD COMPUTINGijasa
Cloud computing is an anthology in which one or more computers are connected in a network. Cloud computing is a cluster of lattice computing, autonomic computing and utility computing. Cloud provides an on demand services to the users. Many numbers of users access the cloud to utilize the cloud resources. The security is one the major problem in cloud computing. Hence security is a major issue in cloud computing. Providing security is a major requirement of cloud computing. The study enclose all the security issues and attack issues in cloud computing.
TOWARD ORGANIC COMPUTING APPROACH FOR CYBERNETIC RESPONSIVE ENVIRONMENTijasa
The developpment of the Internet of Things (IoT) concept revives Responsive Environments (RE) technologies. Nowadays, the idea of a permanent connection between physical and digital world is technologically possible. The capillar Internet relates to the Internet extension into daily appliances such as they become actors of Internet like any hu-man. The parallel development of Machine-to-Machine
communications and Arti cial Intelligence (AI) technics start a new area of cybernetic. This paper presents an approach for Cybernetic Organism (Cyborg) for RE based on Organic Computing (OC). In such approach, each appli-ance is a part of an autonomic system in order to control a physical environment.The underlying idea is that such systems must have self-x properties in order to adapt their behavior to
external disturbances with a high-degree of autonomy.
A STUDY ON DEVELOPING A SMART ENVIRONMENT IN AGRICULTURAL IRRIGATION TECHNIQUEijasa
Maintaining a good irrigation system is a necessity in today’s water scarcity environment. This paper describes a new approach for automated Smart Irrigation (SIR) system in agricultural management. Using
various types of sensors in the crop field area, temperature and moisture value of the soil is monitored.Based on the sensed data, SIR will automatically decide about the necessary action for irrigation and also notifies the user. The system will also focus on the reduction of energy consumption by the sensors during communication.
A REVIEW ON DDOS PREVENTION AND DETECTION METHODOLOGYijasa
Denial of Service (DoS) or Distributed-Denial of Service (DDoS) is major threat to network security.
Network is collection of nodes that interconnect with each other for exchange the Information. This
information is required for that node is kept confidentially. Attacker in network computer captures this
information that is confidential and misuse the network. Hence security is one of the major issues. There
are one or many attacks in network. One of the major threats to internet service is DDoS (Distributed
denial of services) attack. DDoS attack is a malicious attempt to suspending or interrupting services to
target node. DDoS or DoS is an attempt to make network resource or the machine is unavailable to its
intended user. Many ideas are developed for avoiding the DDoS or DoS. DDoS happen in two ways
naturally or it may due to some botnets .Various schemes are developed defense against to this attack.
Main idea of this paper is present basis of DDoS attack. DDoS attack types, DDoS attack components,
survey on different mechanism to prevent DDoS
The smart mobile terminal operator platform Android is getting popular all over the world with its wide variety of applications and enormous use in numerous spheres of our daily life. Considering the fact of increasing demand of home security and automation, an Android based control system is presented in this paper where the proposed system can maintain the security of home main entrance and also the car door lock. Another important feature of the designed system is that it can control the overall appliances in a room. The mobile to security system or home automation system interface is established through Bluetooth. The hardware part is designed with the PIC microcontroller.
The World Wide Web is booming and radically vibrant due to the well established standards and widely accountable framework which guarantees the interoperability at various levels of the application and the society as a whole. So far, the web has been functioning at the random rate on the basis of the human intervention and some manual processing but the next generation web which the researchers called semantic web, edging for automatic processing and machine-level understanding. The well set notion, Semantic Web would be turn possible if only there exists the further levels of interoperability prevails among the applications and networks. In achieving this interoperability and greater functionality among the applications, the W3C standardization has already released the well defined standards such as RDF/RDF Schema and OWL. Using XML as a tool for semantic interoperability has not achieved anything effective and failed to bring the interconnection at the larger level. This leads to the further inclusion of inference layer at the top of the web architecture and its paves the way for proposing the common design for encoding the ontology representation languages in the data models such as RDF/RDFS. In this research article, we have given the clear implication of semantic web research roots and its ontological background process which may help to augment the sheer understanding of named entities in the web.
Wireless sensor networks provide ubiquitous computing systems in various open environments. In the
environment, sensor nodes can easily be compromised by adversaries to generate injecting false data
attacks. The injecting false data attack not only consumes unnecessary energy in en-route nodes, but also
causes false alarms at the base station. To detect this type of attack, a bandwidth-efficient cooperative
authentication (BECAN) scheme was proposed to achieve high filtering probability and high reliability
based on random graph characteristics and cooperative bit-compressed authentication techniques. This
scheme may waste energy resources in en-route nodes due to the fixed number of forwarding reports. In
this paper, our proposed method effectively selects a dynamic number of forwarding reports in the source
nodes based on an evaluation function. The experimental results indicate that our proposed method
enhances the energy savings while maintaining security levels as compared to BECAN.
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
Wireless sensor networks (WSNs) are regularly deployed in harsh and unattended environments, and
sensor nodes are easily exposed to attacks due to the random arrangement of the sensor field. An attacker
can inject fabricated reports from a compromised node with false votes and false vote-based reports. The
false report attacks can waste the energy of the intermediate nodes, shortening the network lifetime.
Furthermore, false votes cause the filtering out of legitimate reports. A probabilistic voting-based filtering
scheme (PVFS) was proposed as a countermeasure against this type of attacks by Li and Wu. PVFS uses a
vote threshold, a security threshold, and a verification node. The scheme does not make additional use
energy or communications resources because the verification node and threshold values are fixed. There
needs to be a verification node selection method that considers the energy resources of the node. In this
paper, we propose a verification path election scheme based on a fuzzy logic system. In the proposed
scheme, one node transmits reports in the node with a strong state through a fuzzy logic system after which
a neighbor is selected out of two from the surroundings. Experimental results show that the proposed
scheme improves energy savings up to maximum 13% relative to the PVFS.
In this paper a novel intelligent soft computing based cryptographic technique based on synchronization of
two chaotic systems (CSCT) between sender and receiver has been proposed to generate session key using
Pecora and Caroll (PC) method. Chaotic system has some unique features like sensitive to initial
conditions, topologically mixing; and dense periodic orbits. By nature, the Lorenz system is very sensitive
to initial conditions meaning that the error between attacker and receiver is going to grow exponentially if
there is a very slight difference between their initial conditions. All these features make chaotic system as
good alternatives for session key generation. In the proposed CSCT few parameters ( , b , r , x1 ,y2 and z2 )
are being exchanged between sender and receiver. Some of the parameter which takes major roles to form
the session key does not get transmitted via public channel, sender keeps these parameters secret. This way
of handling parameter passing mechanism prevents any kind of attacks during exchange of parameters like
sniffing, spoofing or phishing.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
An Enhanced Bio-Stimulated Methodology to Resolve Shop Scheduling Problems
1. International Journal of Ambient Systems and Applications (IJASA) Vol.1, No.2, June 2013
27
An Enhanced Bio-Stimulated Methodology to
Resolve Shop Scheduling Problems
Divya. P, Narendhar. S and Ravibabu. V
Department of Computer Applications, School of Computer Science and Engineering,
Bharathiar Universiy, Coimbatore, Tamil Nadu, INDIA
Abstract
This paper symbolizes the efficiency of Customized Bacterial Foraging Optimization algorithm.
In this research work, Bacterial Foraging Optimization was combined with Ant Colony Optimization and
a new technique Customized Bacterial Foraging Optimization for solving Job Shop Scheduling, Flow
Shop Scheduling and Open Shop Scheduling problems were suggested. The Customized Bacterial
Foraging Optimization was tested on the Benchmark instances and randomly created instances. From the
implementation of this research work, it could be observed that the proposed Customized Bacterial
Foraging Optimization was effective than Bacterial Foraging Optimization algorithm in solving Shop
Scheduling Problems. Customized Bacterial Foraging Optimization can also be used to resolve real
world Shop Scheduling Problems.
Keywords: Ant Colony Optimization (ACO), Bacterial Foraging Optimization (BFO), Job
Shop Scheduling Problem (JSSP), Flow Shop Scheduling Problem (FSSP), Open Shop
Scheduling Problem (OSSP), Customized Bacterial Foraging Optimization (CBFO)
1. Introduction
1.1 Ant Colony Optimization
ACO algorithm first proposed by M. Dorigo, in 1992 [40]. It is a metaheuristic in which
a colony of ants capable of finding shortest trail from their nest to food sources using
pheromone examinations. Real ants are not only capable of finding the shortest path from a food
source to the nest as shown in the Figure 1 (Colorni et al., 1993; Dorigo and Gambardella, 1997;
Holldobler and Wilson, 1990) without using visual prompts, but also they are competent of
adapting to changes in the environment The probability that the ants coming delayed choose the
path is proportional to the amount of pheromone on the path, earlier dropped by other ants. For
example, they will get a new shortest path once the previous one is no longer possible.
1.2 Bacterial Foraging Optimization
BFO was introduced by Kevin M. Passino in 2000 for distributed optimization problems
[9]. Bacterial Foraging Optimization (BFO) algorithm is a novel evolutionary calculation
algorithm suggested based on the foraging activities of Escherichia coli (E. coli) bacteria living
in human intestine [19]. The BFO algorithm is a biologically enthused computing method which
is supported on mimicking the foraging activities of E. coli bacteria.
2. International Journal of Ambient Systems and Applications (IJASA) Vol.1, No.2, June 2013
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The BFO Algorithm associates to the field of Bacteria Optimization Algorithms and
Swarm Optimization. BFO algorithm is successfully applied in several real world problems and
improved BFO metaheuristics were applied to optimization problems.
Framework for BFO algorithm
• Input the bacterial foraging parameters and independent variable, then specify inferior
and superior limits of the variables and begin the elimination-dispersal steps,
reproduction and chemotactic.
• Generate the locations of the independent variable arbitrarily for a population of
bacteria. Estimate the intention value of each bacterium.
• Change the position of the variables for all the bacteria with the tumbling or swimming
procedure .Perform reproduction and elimination procedure.
• If the maximum number of chemotactic, reproduction and elimination-dispersal steps is
achieved, then output the variable corresponding to the overall best bacterium;
Otherwise, do again the procedure by changing the position of the variables for all the
bacteria with the tumbling /swimming procedure.
Figure 1: Double Bridge Experiment
1.3 Job Shop Scheduling Problem (JSSP)
Job : A piece of work that goes through series of operations.
Shop : A place for manufacturing or repairing of goods or machinery.
Scheduling : Decision process aiming to deduce the order of processing.
The JSSP is an operation sequencing problem on multiple machine subject to some
precedence constraints among the operations. The JSSP can be explained as a set of n jobs
represented by Jj where j =1,2…n which have to be processed on a set of m machines
represented by Mk where k =1,2….m. Operation of jth
job on the kth
machine will be
represented by Ojk with the processing time pjk [25] .Each job should be processed through the
machines in a exacting order or also known as technological constraint. Once a machine begins
to process a job, no disruption is allowed. The time required for all operations to complete their
processes is called makespan. JSSP are widely known as NP-Hard problem. Figure 2 represents
flow of JSSP.
3. International Journal of Ambient Systems and Applications (IJASA) Vol.1, No.2, June 2013
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Figure 2: Flow of JSSP
Constraints
The JSSP focuses to two constraints, they are
• Operation precedence constraint
The operation precedence constraint on the job is that the arrangement of operations of
job is fixed and the processing of an operation cannot be interrupted and concurrent.
• Machine processing constraint
The machine processing constraint is that only a solitary job can be processed at the
identical time on the identical machine.
The main factor affecting to the JSSP is the nature of job shop. In static and deterministic
job shop, all jobs are obtainable for processing without delay, and no fresh jobs appear over
time. In the dynamic probabilistic job shop, jobs arrive arbitrarily over time, and processing
times are probabilistic. This is more practical job shop circumstances but more complicated to
solve it.
1.4 Flow Shop Scheduling Problem
Johnson’s Rule (Johnson, 1954) has been the basis of various FSSP heuristics. Palmer
(1965) first proposed a heuristic for the FSSP to minimize makespan. FSSP are described by a
set of n jobs, where every job has to be processed in the same order on a given number of m
machines. Each machine can process only one job at a time. The factors tij,1≤ i ≤ n ,1≤ j ≤ m,
indicate The processing time of job i on machine j [2]. The FSSP is a set of jobs that flow
through multiple stages in the similar order as shown in the Figure 3.
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Figure 3: Flow of FSSP
1.5 Open Shop Scheduling Problems
The Open Shop Scheduling Problems can be categorized as n x m, where 'n' is the
number of Jobs (J= {j1, j2,.., jn}) can be processed on 'm' number of Machines (M= {m1,
m2,...,mm}). Every machine can process at most single operation at a moment and each job can
be processed by at most single machine at a time. For every machine the order in which the jobs
are processed on the machine (Machine Orders) and for all jobs the order in which this job is
processed through the machines (Job Orders) can be selected arbitrarily.
Constraints
• No machine can process more than single operation at the similar time and
• No job can be processed by more than single machine at the similar time.
In this research work, BFO algorithm was hybridized with ACO and a new Customized
Bacterial Foraging Optimization (CBFO) algorithm was proposed. Both BFO and CBFO
algorithm were applied to Admas, Balas and Zawaxk (ABZ), Carlier (Car), Taillard (TA) and
Ravibabu, Narendhar and Divya (RND) randomly created instances. The results obtained by
CBFO algorithm is compared and analyzed with BFO and existing algorithms.
2. Related Works
E. Taillard [1989] has proposed a paper about Benchmarks’ for Basic Scheduling Problems.
In this paper Taillard talked on the subject of 260 scheduling problems whose size is greater
than that of other examples. In this paer he explained about Job Shop, Flow Shop, Open Shop
Scheduling Problems. The objective of this paper is to mininmization of makespan [12]
Ashwani Kumar Dhingra has discussed about scheduling problems. He gave a brief
explanation about scheduling problems, Significance of Scheduling, Scheduling in a
Manufacturing System and Classification of scheduling problems based on requirement
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generations. Problems up to 200 jobs and 20 machines for instances expanded by Taillard
(1993) have been solved and proposed metaheuristics can be tested on various problems [5].
Mahanim Omar, Adam Baharum, Yahya Abu Hasan (2006) have proposed a paper about A
Job Shop Scheduling Problem (JSSP) using Genetic Algorithm. Job shop problems are
generally known as a NP-Hard problem. In this paper they have formed a preliminary
population arbitrarily together with the result achieved by some familiar priority rules such as
shortest processing time and longest processing time. This is used to reduce the objective
function [25].
David Applegate, William Cook (1991) have proposed a paper about A Computational
Study of the JSSP. They tested performance of JSSP with some model instances. MT-10 is a
well-known 10 by 10 problem of Muth and Thompson; ABZ5 and ABZ6 are two problems
from Admas, Balas and Zawaxk; the problems LA19 and LA20 are problems of Lawrence.
They compared their results with best solution [10].
According to Hela Boukef, Mohamed Benrejeb and Pierre Borne [2006-2007] have
proposed a new genetic algorithm coding is suggested in this paper to solve flow-shop
scheduling problems. To explain the effectiveness of the considered approach with decrease of
different costs related to every problem as a scope. Multi-objective optimization is thus, used
and its performances confirmed. The standard range of this technique, based on natural variety
of mechanism, is the development of robustness and balance among cost and performance [15].
AndreasFink, StefanVoß (2001) have projected a paper about Solving the Continuous Flow
Shop Scheduling Problem by Metaheuristics. This problem is used to locate a combination of
jobs to be processed consecutively on a number of machines under the constraint that the
processing of every job has to be uninterrupted with respect to the purpose of minimizing the
entire processing time (flow-time). i.e., once the processing of a job begins, there must not be
any waiting times between the processing of any consecutive tasks of this job [2].
According to Samia kouki, Mohamed Jemni, Talel Ladhari (2011) have proposed a paper
about Solving the Permutation Flow Shop Problem with Makespan principle using Grids. The
optimization of scheduling problems is stand on different criteria to optimize. One of the most
significant criteria is to reduce the completion time of the final task on the end machine called
makespan. They offered a parallel algorithm for solving the permutation flow shop problem.
This is used to minimizing the total makespan of the tasks by using Branch and Bound method
to find optimal solutions [37]
According to Peter Brucker, Johann Hurink, Bernd Jurisch and Birgit Gstmann (1995)
have proposed a paper about fundamental concepts of branch & bound algorithm. The branch and
bound algorithm for the OSSP is based on a disjunctive graph formulation. The problem
determined a possible mixture of the machine and job orders which minimizes a certain
objective function.
Ching-Fang Liaw (1999) has talk on the subject of the growth and function of a Hybrid
Genetic Algorithm (HGA) to the OSSP is based on Tabu Search (TS) into a basic Genetic
Algorithm (GA). The local enhancement method enables the HGA algorithm to execute genetic
search in excess of the subspace of local optima. Benchmark problems of OSSP are tested by
using these algorithms. The results were compared with other algorithms also [7].
Jing Dang, Anthony Brabazon, Michael O‟Neill, and David Edition (2008) have discussed
a paper regarding Bacterial Foraging Optimization (BFO) algorithm. This technique was
implemented to resolve the parameter estimation of an EGARCH-M model.During the lifetime
of E.coli bacteria, they undergo different stages such as chemotaxis, reproduction and
6. International Journal of Ambient Systems and Applications (IJASA) Vol.1, No.2, June 2013
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elimination-dispersal. BFO algorithm is applied to solve various real world problems [19]
Chunguo Wu, Na Zhang, Jingqing Jiang, Jinhui Yang, and Yanchun Liang (2007) described
Bacterial Foraging algorithm is a novel evolutionary computation algorithm. This is based on
the foraging behaviour of E.coli bacteria living in human intestine. BFO is fundamentally an
arbitrary search algorithm. This better algorithm is applied to Job Shop Scheduling Benchmark
problems [9].
S. Subramanian and S. Padma (2011) have proposed a paper about the selection behaviour of
bacteria leans to eliminate poor foraging strategies and get better successful foraging strategies.
The E.coli bacterium has a control system that enables it to look for food and try to keep away
from noxious substances. BFO is used to reduce the cost and improves the competence
concurrently by using a multi objective based bacterial foraging algorithm [35]
According to James Montgomery, cardc Fayad and Sarja Petrovic have proposed a paper
about Solution Representation for Job Shop Scheduling Problems in ACO. The result produces
improved explanation more quickly than the usual approach. They created resolutions by
creating a permutation of the operations, from which a deterministic algorithm can produce the
real schedule [17]
Katie Kinzler (2008), has proposed a dissertation about Mathematical Modeling of Ant
Pheromones: Purpose of Optimum pheromone Evaporation Rate and replication of Pheromone
Tracking Abilities. There are more varieties of technique used by ant to communicate as well as
a variety of reasons for communication. These communications involves stroking, gasping,
antenna movements, and streaking of chemicals. These chemicals are known as pheromones.
This is the major form of communication used by ants [22]
3. Customized Bacterial Foraging Methodology For JSSP, FSSP &
OSSP
The objectives of this research paper are
• To propose and implement Customized Bacterial Foraging Optimization (CBFO) to
solve JSSP, FSSP and OSSP.
• CBFO is to find a schedule that reduces the makespan of the jobs.
• To examine the efficiency of CBFO in solving benchmark instances of JSSP, FSSP and
OSSP.
• To analyze and compare the performance of the proposed CBFO with BFO in solving
JSSP, FSSP and OSSP.
3.1 Customized Bacterial Foraging Optimization (CBFO)
The activity of ant structure is included in tumble part of BFO algorithm, to formulate it
as a CBFO. Each ant creates a tour by repeatedly applying a stochastic greedy rule, which is
called the state transition rule.
(r, u) represents an boundary between point r and u, and τ(r, u) represents the pheromone on
border (r, u). η(r, u) is the attraction of border (r, u), which is habitually described as the
contrary of the length of edge (r, u). q is a arbitrary number uniformly distributed in [0, 1], q0 is
a user-defined parameter with (0≤q0≤1), β is the parameter controlling the relative importance
(1)
7. International Journal of Ambient Systems and Applications (IJASA) Vol.1, No.2, June 2013
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of the desirability. J (r) is the set of edges available at decision point r. S is a arbitrary variable
selected according to the probability distribution given below.
The mixture approach used on top of this is also called ‘roulette wheel’ selection since
its mechanism is an imitation of the process of a roulette wheel [16].
While ant goes for a search it will drop a certain amount of pheromone. It is a
continuous progression, but we can regard it as a discrete release by some rules. There are two
kinds of pheromone update strategies, called local updating rule and the global updating rule.
Local updating rule
While ant generating its tour, ant will adjust the quantity of pheromone on the passed
perimeters by applying the local updating rule.
Where ρ is the coefficient representing pheromone evaporation (note:0< ρ < 1 ).
Global updating rule
Once all ants have entered at their target, the amount of pheromone on the boundary is
modified again by applying the global updating rule.
Where
Here 0<α<1 is the pheromone decompose parameter, and Lgb is the distance of the
globally most excellent tour from the starting of the examination. ∆τ(r; s) is the pheromone
addition on edge (r, s). We can see that only the ant that discovers the global best tour can attain
the pheromone increase [15].
In BFO, the objective is to discover the least of J(θ),θ ∈RD
, where we do not have the
gradient information J(θ). Suppose θ is the location of the bacterium and J(θ) stands for a
nutrient profile, i.e.,J(θ) < 0, J(θ)=0 and J(θ)> 0 stand for the presence of nutrients, a neutral
medium and noxious substances correspondingly. The bacterium will try to go towards
increasing concentrations of nutrients (i.e. find lower values of J), search for ways out of neutral
media and avoid noxious substances (away from positions where J > 0). It equipment a kind of
biased random walk.
The mathematical swarming (cell-cell signalling) function can be characterized by:
Where ║.║ is the Euclidean norm, Wa and Wr are actions of the width of the attractant and
repulsive signals correspondingly, M measures the magnitude of the cell-cell signaling
consequence [15].
(3)
(4)
(5)
(6)
(2)
8. International Journal of Ambient Systems and Applications (IJASA) Vol.1, No.2, June 2013
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The above State Transition rule of ant in ACO is included in the tumble. CBFO
methodology is implemented with no swarming effect (ie) jcc=0 [19]. Here time is considered as
cost. At some point in the lifetime of E-Coli bacteria they undertake different phases such as
Chemotactics, Reproduction and Elimination-Dispersal. When compared with ACO and BFO,
CBFO attains high level of SHA1PRNG algorithm incase of reproduction, elimination-
dispersal.
CBFO Algorithm
for Elimination-dispersal do
for Reproduction do
for Chemotaxis do
for Bacterium i do
Tumble: Generate a secure random vector q ∈ decimal value.
If q < q0 then
Generate a secure random vector l ∈ operation, according to
pheromone value ph[job][operation] based on equation 1.
Else
Generate a secure random vector l ∈ operation, according to
pheromone value ph[job][operation] based on equation 2.
end
Move: Generate a secure random vector lnew ∈ operation.
Swim:
if time[job][l] < time[job][ lnew] then
current_operation = l
Else
current_operation = l new
end
end
end
end
Sort bacteria in order of ascending time Jst. The Sr = S/2 bacteria with The peak J value
die and other Sr bacteria with the preeminent value split Update value of J and Jst
consequently.
end
Eliminate and disperse the bacteria to arbitrary locations on the optimization domain with
probability ped. Update equivalent J and Jst.
End
Note: Parameters are described below in Nomenclature
9. International Journal of Ambient Systems and Applications (IJASA) Vol.1, No.2, June 2013
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3.2 Nomenclature
Jcc - Health of bacterium i
ωattract - Width of attractant
ωrepellan t - Width of repellent
J i
health - Health of bacterium i
L - Counter for elimination- dispersal step
Ped - Probability of occurrence of elimination-dispersal events
S - Population of the E.coli bacteria
4. Implementation Results and Discussion
This paper discusses and compares the result of the implementation of BFO and
proposed CBFO algorithm in solving the Benchmark instances of JSSP, FSSP and OSSP.
Admas, Balas and Zawaxk (ABZ) Benchmark problems [30], Ravibabu, Narendhar and
Divya (RND) randomly created instance for JSSP, Carlier (car) benchmark problems [30] and
RND for FSSP and Taillard benchmark problems and RND for OSSP were solved in this
research work. Benchmark instances were taken from Operations Research (OR) Library to test
the efficiency of proposed CBFO. The proposed CBFO algorithm gave reasonable solution for
most runs for the constant values ρ=0.1, β=1.0, α=0.1, q0=0.8, τ=0.5. . The result achieved by
proposed CBFO algorithm was compared with BFO and existing algorithms. The CBFO
algorithm gave a best makespan for most of the problems.
4.1 JSSP Comparison Results for ABZ Instances
The best result for JSSP achieved from proposed CBFO algorithm and BFO algorithm
were compared with optimal value of ABZ Instances are shown in Table 1. The variation of
CBFO algorithm is due to Time constraint and limited iterations. The proposed CBFO can be
improved to achieve best solution by including the swarming technique and also by adjusting
constant values used in the algorithms. The Figure 4 shows the graphical representation of
Table 1.
Table 1: JSSP Comparison Results for ABZ Instances
INSTANCE SIZE BFO OPTIMAL[9] CBFO
ABZ 5 10 *10 1323 1234 1320
ABZ 5 10 *10 1012 943 975
ABZ 5 20 * 15 787 668 782
ABZ 5 20 * 15 822 687 790
ABZ 5 20 * 15 856 707 835
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0
200
400
600
800
1000
1200
1400
10 *10 10 *10 20 * 15 20 * 15 20 * 15
ABZ 5 ABZ 5 ABZ 5 ABZ 5 ABZ 5
ABZ Instances for JSSP
Makespan
BFO
OPTIMAL[9]
CBFO
Figure 4: Graphical representation of results for ABZ Instances
4.2 JSSP Comparison Results for RND Instances
The best result obtained from BFO algorithm and optimal values are compared with
proposed CBFO algorithm of RND Instances is shown in Table 2. The Figure 5 shows the
graphical representation of Table 2.
Table 2: JSSP Comparison Results for RND Instances
INSTANCE SIZE BFO CBFO
RND 10 10*10 740 709
RND 20 20*20 1762 1746
RND 30 30*30 2652 2601
RND 40 40*40 3574 3457
RND 50 50*50 4849 4685
0
1000
2000
3000
4000
5000
6000
10*10 20*20 30*30 40*40 50*50
RND 10 RND 20 RND 30 RND 40 RND 50
RND Instances for JSSP
Makespan
BFO
CBFO
Figure 5: Graphical representation of results for RND Instances
11. International Journal of Ambient Systems and Applications (IJASA) Vol.1, No.2, June 2013
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4.3 FSSP Comparison Results for Car Instances
The best result obtained from proposed CBFO algorithm, BFO algorithm were compared
with Lower Bound (LB), Upper Bound (UB) [22] of Carlier Instances are shown in Table 3. The
Figure 6 shows the graphical representation of Table 3.
Table 3: FSSP Comparison Results for Car Instances
INSTANCE SIZE LB UB BFO CBFO
Car 1 11 * 5 7038 7817 7452 7285
Car 2 13 * 4 7166 7940 8051 7640
Car 3 12 * 5 7312 7779 7900 7930
Car 4 14 * 4 8003 8679 8707 8344
Car 5 10 * 6 7720 8773 8094 8365
Car 6 8 *9 8505 10211 9068 9656
Car 7 7 * 7 6590 7043 6868 6940
Car 8 8 * 8 8366 9696 8703 9316
0
2000
4000
6000
8000
10000
12000
11 * 5 13 * 4 12 * 5 14 * 4 10 * 6 8 *9 7 * 7 8 * 8
Car 1 Car 2 Car 3 Car 4 Car 5 Car 6 Car 7 Car 8
Carlier Instances for FSSP
Makespan
LB
UB
BFO
CBFO
Figure 6: Graphical representation of results for Carlier Instances
4.4 FSSP Comparison Results for RND Instances
The best result obtained from proposed CBFO algorithm is compared with best result
obtained from BFO algorithm in solving FSSP for RND instances are shown in Table 4. The
Figure 7 shows the graphical representation of Table 4.
12. International Journal of Ambient Systems and Applications (IJASA) Vol.1, No.2, June 2013
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Table 4: FSSP Comparison Results for RND Instances
INSTANCE SIZE BFO CBFO
RND 5 5 * 5 463 461
RND 6 6 * 6 571 563
RND 7 7 * 7 613 607
RND 10 10 * 10 1055 1050
RND 25 25 * 25 2920 2940
0
500
1000
1500
2000
2500
3000
3500
5 * 5 6 * 6 7 * 7 10 * 10 25 * 25
RND 5 RND 6 RND 7 RND 10 RND 25
RND Insta nce s for FSSP
Makespan
BFO
CBFO
Figure 7: Graphical representation of results for RND Instances
4.5 OSSP Comparison Results for TA Instances
The best solution obtained from proposed CBFO algorithm is compared with best
solution obtained from Lower Bound (LB), Upper Bound (UB) of Taillard Instances and BFO
algorithm in solving OSSP are shown in Table 5. The Figure 8 shows the graphical
representation of Table 5.
Table 5: OSSP Comparison Results for TA Instances
INSTANCE LB UB BFO CBFO
Ta 4*4 186 193 194 192
Ta 4*4 229 236 243 235
Ta 5*5 321 328 377 357
Ta 5*5 349 353 403 395
Ta 7*7 416 419 567 549
Ta 7*7 398 400 530 518
13. International Journal of Ambient Systems and Applications (IJASA) Vol.1, No.2, June 2013
39
0
100
200
300
400
500
600
Ta 4*4 Ta 4*4 Ta 5*5 Ta 5*5 Ta 7*7 Ta 7*7
Taillard Instances for OSSP
Makespan
LB
UB
BFO
CBFO
Figure 8: Graphical representation of results for Taillard Instances
4.6 OSSP Comparison Results for RND Instances
BFO algorithm is compared with proposed CBFO algorithm in solving OSSP for RND
instances are shown in Table 6. The Figure 9 shows the graphical representation of Table 6.
Table 6: OSSP Comparison Results for RND Instances
INSTANCE SIZE ACO BFO CBFO
RND 4 4 * 4 212 210 207
RND 5 5 * 5 296 290 287
RND 7 7 * 7 510 498 486
RND 10 10 * 10 834 799 787
RND 15 15 * 15 1188 1167 1121
RND 20 20 * 20 1628 1446 1350
0
200
400
600
800
1000
1200
1400
1600
1800
4 * 4 5 * 5 7 * 7 10 * 10 15 * 15 20 * 20
R N D 4 R N D 5 R N D 7 R N D 10 R N D 15 R N D 20
R N D In sta n ce s fo r O S S P
Makespan
A C O
B F O
C B F O
Figure 9: Graphical representation of results for RND Instances
14. International Journal of Ambient Systems and Applications (IJASA) Vol.1, No.2, June 2013
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5. Conclusions
In this research work, BFO algorithm is hybridized with ACO and a new technique CBFO
was proposed for solving different instances of JSSP, FSSP and OSSP. The proposed CBFO
algorithm was investigated through the performance of several runs on well-known test
problems of different sizes, which were taken from OR library, which is the primary repository
for such problems. The results obtained by the proposed CBFO algorithm for JSSP, FSSP and
OSSP can achieve the best and near best solution quality for most of the instances and RND
instances.
The implementation of the CBFO algorithm for huge size instances can be done by raising
the number of iterations to get best solutions. The proposed CBFO for JSSP, FSSP and OSSP
can be improved to achieve best solution by including the swarming technique and also by
adjusting constant values used in the algorithms. As a future work, Flexible Job Shop
Scheduling, Flexible Flow Shop Scheduling and Flexible Open Shop Scheduling problems can
also be solved using proposed CBFO algorithm.
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Authors
Ms. P. Divya received her B. Sc Degree in Computer Science,
Masters Degree (MCA) in Computer Applications in 2009 and 2012
respectively from Bharathiar University, Coimbatore, Tamil Nadu,
India. Her area of interest is Bio-Inspired computing
Mr. S. Narendhar received his B. Sc Degree in Computer
Technology from Anna University, Coimbatore, India in the year
2009 and Masters Degree (MCA) in Computer Applications from
Bharathiar University, Coimbatore, India in the year 2012. His area
of interest includes Agent based computing and Bio-inspired
computing. He has attended National Conferences.
Mr. V. Ravibabu received his B.Sc Degree in Computer
Science and MCA Degree in Computer Applications in 2009
and 2012 respectively, from Bharathiar University, Coimbatore,
India. His area of interest includes Agent based computing
and Bio-inspired computing. He has attended National
Conferences. He is a member of International Association of
Engineers.