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.
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.
Measurement of farm level efficiency of beef cattle fattening in west java pr...Alexander Decker
This document summarizes a study on measuring farm-level efficiency of beef cattle fattening operations in West Java Province, Indonesia. The study analyzed data from 100 beef cattle farmers to estimate technical efficiency levels and determinants. Results found the average technical efficiency was 0.77, meaning output could increase 23% with current technology. Education levels, experience, cattle ownership numbers, and access to credit significantly impacted technical inefficiency. The study concluded that improving farmer knowledge through extension and training could boost technical efficiency.
This document describes using a hybrid bacterial foraging particle swarm optimization (BF-PSO) algorithm to tune the parameters of a PID controller (Kp, Ki, Kd) for a stable linear time invariant system. The BF-PSO algorithm combines bacterial foraging optimization and particle swarm optimization to find optimal PID parameters by minimizing error criteria like ISE, IAE, ITAE and MSE. The transfer function of the plant is given and the PID controller is tuned using BF-PSO to minimize these performance indices and improve the closed loop step response.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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.
IRJET- Performance Analysis of Optimization Techniques by using ClusteringIRJET Journal
This document discusses optimization techniques for clustering algorithms. It introduces fuzzy bee colony optimization (FBCO) and compares its performance to other swarm algorithms like fuzzy c-means (FCM) and fuzzy particle swarm optimization (FPSO). FBCO is motivated by the natural behaviors of bee colonies and aims to avoid local minima problems. The document provides background on clustering, describes the FCM and FPSO algorithms, and proposes a FBCO algorithm to improve clustering performance.
Prediction of soil liquefaction using genetic programmingAhmed Ebid
DOI: 10.13140/2.1.2034.4644
In most geotechnical problems, it is too difficult to predict soil and structural behavior accurately, because of the large variation in soil parameters and the assumptions of numerical solutions. But recently many geotechnical problems are solved using Artificial Intelligence (AI) techniques, by presenting new solutions or developing existing ones. Genetic Programming, (GP), is one of the most recently developed (AI) techniques based on Genetic Algorithm (GA) technique. In this research, GP technique is utilized to develop prediction criteria for liquefaction phenomena in cohesivless soils using collected historical records. The liquefaction formula is developed using special software written by the authors in "Visual C++" language. The accuracy of the developed formula was also compared with earlier prediction methods.
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
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.
Measurement of farm level efficiency of beef cattle fattening in west java pr...Alexander Decker
This document summarizes a study on measuring farm-level efficiency of beef cattle fattening operations in West Java Province, Indonesia. The study analyzed data from 100 beef cattle farmers to estimate technical efficiency levels and determinants. Results found the average technical efficiency was 0.77, meaning output could increase 23% with current technology. Education levels, experience, cattle ownership numbers, and access to credit significantly impacted technical inefficiency. The study concluded that improving farmer knowledge through extension and training could boost technical efficiency.
This document describes using a hybrid bacterial foraging particle swarm optimization (BF-PSO) algorithm to tune the parameters of a PID controller (Kp, Ki, Kd) for a stable linear time invariant system. The BF-PSO algorithm combines bacterial foraging optimization and particle swarm optimization to find optimal PID parameters by minimizing error criteria like ISE, IAE, ITAE and MSE. The transfer function of the plant is given and the PID controller is tuned using BF-PSO to minimize these performance indices and improve the closed loop step response.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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.
IRJET- Performance Analysis of Optimization Techniques by using ClusteringIRJET Journal
This document discusses optimization techniques for clustering algorithms. It introduces fuzzy bee colony optimization (FBCO) and compares its performance to other swarm algorithms like fuzzy c-means (FCM) and fuzzy particle swarm optimization (FPSO). FBCO is motivated by the natural behaviors of bee colonies and aims to avoid local minima problems. The document provides background on clustering, describes the FCM and FPSO algorithms, and proposes a FBCO algorithm to improve clustering performance.
Prediction of soil liquefaction using genetic programmingAhmed Ebid
DOI: 10.13140/2.1.2034.4644
In most geotechnical problems, it is too difficult to predict soil and structural behavior accurately, because of the large variation in soil parameters and the assumptions of numerical solutions. But recently many geotechnical problems are solved using Artificial Intelligence (AI) techniques, by presenting new solutions or developing existing ones. Genetic Programming, (GP), is one of the most recently developed (AI) techniques based on Genetic Algorithm (GA) technique. In this research, GP technique is utilized to develop prediction criteria for liquefaction phenomena in cohesivless soils using collected historical records. The liquefaction formula is developed using special software written by the authors in "Visual C++" language. The accuracy of the developed formula was also compared with earlier prediction methods.
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
An invasive weed optimization (iwo) approachiaemedu
1. The document describes an Invasive Weed Optimization (IWO) approach for solving multi-objective job shop scheduling problems. IWO is a metaheuristic algorithm inspired by how weeds colonize an area.
2. It summarizes the IWO algorithm which initializes a population of weeds randomly, allows each weed to reproduce seeds based on fitness, and spatially disperses the seeds to new locations with varying variance to simulate weed colonization.
3. The paper proposes applying IWO to minimize makespan, tardiness, and mean flowtime for multi-objective job shop scheduling. It evaluates solutions using fuzzy dominance to handle the multi-objective nature of the problem.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This document presents an overview of cloud computing concepts including cloud architecture, deployment models, service models, characteristics, job scheduling, virtualization, energy conservation, and network security. It discusses key cloud computing topics such as Infrastructure as a Service, Platform as a Service, Software as a Service, public clouds, private clouds, hybrid clouds, community clouds, resource pooling, broad network access, on-demand self-service, and measured service. Virtualization concepts like hypervisors, virtual machine monitors, and virtual network models are also covered.
REVIEW PAPER on Scheduling in Cloud ComputingJaya Gautam
This document reviews scheduling algorithms for workflow applications in cloud computing. It discusses characteristics of cloud computing, deployment and service models, and the importance of scheduling in cloud computing. The document analyzes several scheduling algorithms proposed in literature that consider parameters like makespan, cost, load balancing, and priority. It finds that algorithms like Max-Min, Min-Min, and HEFT perform better than traditional algorithms in optimizing these parameters for workflow scheduling in cloud environments.
A Novel Approach for Measuring Electrical Impedance Tomography for Local Tiss...CSCJournals
This paper proposes a novel approach for measuring Electrical Impedance Tomography (EIT) of a living tissue in a human body. EIT is a non-invasive technique to measure two or three-dimensional impedance for medical diagnosis involving several diseases. To measure the impedance value electrodes are connected to the skin of the patient and an image of the conductivity or permittivity of living tissue is deduced from surface electrodes. The determination of local impedance parameters can be carried out using an equivalent circuit model. However, the estimation of inner tissue impedance distribution using impedance measurements on a global tissue from various directions is an inverse problem. Hence it is necessary to solve the inverse problem of calculating mathematical values for current and potential from conducting surfaces. This paper proposes a novel algorithm that can be successfully used for estimating parameters. The proposed novel hybrid model is a combination of an artificial intelligence based gradient free optimization technique and numerical integration. This ameliorates the achievement of spatial resolution of equivalent circuit model to the closest accuracy. We address the issue of initial parameter estimation and spatial resolution accuracy of an electrode structure by using an arrangement called “divided electrode” for measurement of bio-impedance in a cross section of a local tissue.
The document discusses optimization techniques, including genetic algorithms and particle swarm optimization. It provides definitions and classifications of optimization problems and algorithms. Specifically, it describes the implementation of genetic algorithms as follows:
1. Genetic algorithms initialize a random population of solutions and evaluate them to determine fitness.
2. Operators like selection, crossover and mutation are then applied to produce new potential solutions. Selection chooses the fittest for reproduction, crossover combines solutions, and mutation introduces random changes.
3. The process repeats, selecting and breeding new solutions, until a termination condition is met like reaching a maximum number of generations.
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.
An Enhanced Bio-Stimulated Methodology to Resolve Shop Scheduling Problemsijasa
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.
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
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.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
This document discusses using the Cuckoo Optimization Algorithm (COA) to solve a production planning problem. It provides background on COA and how it was applied to optimize a mathematical model of production planning with the goal of minimizing costs. The COA approach found better solutions than Genetic Algorithm and Lingo software in less time. The authors conclude COA is an effective method for solving this type of constrained nonlinear optimization problem.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
This document discusses using the Cuckoo Optimization Algorithm (COA) to solve a production planning problem. It provides background on COA and how it works, modeling the production planning problem with objectives and constraints. The COA is implemented on a 3-product, 5-period example problem. Results show COA finds better solutions faster than Genetic Algorithm and provides answers close to commercial solver Lingo. COA is thus shown to be an effective method for solving this type of constrained nonlinear production planning problem.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution
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.
Modified artificial immune system for single row facility layout problemIAEME Publication
One of the main optimization algorithms currently available in the research field is an Artificial Immune System where abundant applications are using this algorithm for clustering and patter recognition processes. These algorithms are providing more effective optimized results in multi-model optimization problems than Genetic Algorithm.
Modified artificial immune system for single row facility layout problemIAEME Publication
The document describes a modified artificial immune system (MAIS) algorithm for solving the single row facility layout problem (SRFLP). The SRFLP involves arranging machines in a single row to minimize material handling costs. Existing artificial immune system (AIS) algorithms have slow convergence and weak stability. The proposed MAIS uses local search around memory antibodies to improve the AIS algorithm. Simulations show MAIS achieves better results than standard AIS. The MAIS is applied to three types of SRFLP - equal distance, zero distance, and different distance between machines. The objective is to minimize the total material handling cost.
An invasive weed optimization (iwo) approachiaemedu
1. The document describes an Invasive Weed Optimization (IWO) approach for solving multi-objective job shop scheduling problems. IWO is a metaheuristic algorithm inspired by how weeds colonize an area.
2. It summarizes the IWO algorithm which initializes a population of weeds randomly, allows each weed to reproduce seeds based on fitness, and spatially disperses the seeds to new locations with varying variance to simulate weed colonization.
3. The paper proposes applying IWO to minimize makespan, tardiness, and mean flowtime for multi-objective job shop scheduling. It evaluates solutions using fuzzy dominance to handle the multi-objective nature of the problem.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This document presents an overview of cloud computing concepts including cloud architecture, deployment models, service models, characteristics, job scheduling, virtualization, energy conservation, and network security. It discusses key cloud computing topics such as Infrastructure as a Service, Platform as a Service, Software as a Service, public clouds, private clouds, hybrid clouds, community clouds, resource pooling, broad network access, on-demand self-service, and measured service. Virtualization concepts like hypervisors, virtual machine monitors, and virtual network models are also covered.
REVIEW PAPER on Scheduling in Cloud ComputingJaya Gautam
This document reviews scheduling algorithms for workflow applications in cloud computing. It discusses characteristics of cloud computing, deployment and service models, and the importance of scheduling in cloud computing. The document analyzes several scheduling algorithms proposed in literature that consider parameters like makespan, cost, load balancing, and priority. It finds that algorithms like Max-Min, Min-Min, and HEFT perform better than traditional algorithms in optimizing these parameters for workflow scheduling in cloud environments.
A Novel Approach for Measuring Electrical Impedance Tomography for Local Tiss...CSCJournals
This paper proposes a novel approach for measuring Electrical Impedance Tomography (EIT) of a living tissue in a human body. EIT is a non-invasive technique to measure two or three-dimensional impedance for medical diagnosis involving several diseases. To measure the impedance value electrodes are connected to the skin of the patient and an image of the conductivity or permittivity of living tissue is deduced from surface electrodes. The determination of local impedance parameters can be carried out using an equivalent circuit model. However, the estimation of inner tissue impedance distribution using impedance measurements on a global tissue from various directions is an inverse problem. Hence it is necessary to solve the inverse problem of calculating mathematical values for current and potential from conducting surfaces. This paper proposes a novel algorithm that can be successfully used for estimating parameters. The proposed novel hybrid model is a combination of an artificial intelligence based gradient free optimization technique and numerical integration. This ameliorates the achievement of spatial resolution of equivalent circuit model to the closest accuracy. We address the issue of initial parameter estimation and spatial resolution accuracy of an electrode structure by using an arrangement called “divided electrode” for measurement of bio-impedance in a cross section of a local tissue.
The document discusses optimization techniques, including genetic algorithms and particle swarm optimization. It provides definitions and classifications of optimization problems and algorithms. Specifically, it describes the implementation of genetic algorithms as follows:
1. Genetic algorithms initialize a random population of solutions and evaluate them to determine fitness.
2. Operators like selection, crossover and mutation are then applied to produce new potential solutions. Selection chooses the fittest for reproduction, crossover combines solutions, and mutation introduces random changes.
3. The process repeats, selecting and breeding new solutions, until a termination condition is met like reaching a maximum number of generations.
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.
An Enhanced Bio-Stimulated Methodology to Resolve Shop Scheduling Problemsijasa
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.
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
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.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
This document discusses using the Cuckoo Optimization Algorithm (COA) to solve a production planning problem. It provides background on COA and how it was applied to optimize a mathematical model of production planning with the goal of minimizing costs. The COA approach found better solutions than Genetic Algorithm and Lingo software in less time. The authors conclude COA is an effective method for solving this type of constrained nonlinear optimization problem.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
This document discusses using the Cuckoo Optimization Algorithm (COA) to solve a production planning problem. It provides background on COA and how it works, modeling the production planning problem with objectives and constraints. The COA is implemented on a 3-product, 5-period example problem. Results show COA finds better solutions faster than Genetic Algorithm and provides answers close to commercial solver Lingo. COA is thus shown to be an effective method for solving this type of constrained nonlinear production planning problem.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution
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.
Modified artificial immune system for single row facility layout problemIAEME Publication
One of the main optimization algorithms currently available in the research field is an Artificial Immune System where abundant applications are using this algorithm for clustering and patter recognition processes. These algorithms are providing more effective optimized results in multi-model optimization problems than Genetic Algorithm.
Modified artificial immune system for single row facility layout problemIAEME Publication
The document describes a modified artificial immune system (MAIS) algorithm for solving the single row facility layout problem (SRFLP). The SRFLP involves arranging machines in a single row to minimize material handling costs. Existing artificial immune system (AIS) algorithms have slow convergence and weak stability. The proposed MAIS uses local search around memory antibodies to improve the AIS algorithm. Simulations show MAIS achieves better results than standard AIS. The MAIS is applied to three types of SRFLP - equal distance, zero distance, and different distance between machines. The objective is to minimize the total material handling cost.
MULTIPROCESSOR SCHEDULING AND PERFORMANCE EVALUATION USING ELITIST NON DOMINA...ijcsa
Task scheduling plays an important part in the improvement of parallel and distributed systems. The problem of task scheduling has been shown to be NP hard. The time consuming is more to solve the problem in deterministic techniques. There are algorithms developed to schedule tasks for distributed environment, which focus on single objective. The problem becomes more complex, while considering biobjective.This paper presents bi-objective independent task scheduling algorithm using elitist Nondominated
sorting genetic algorithm (NSGA-II) to minimize the makespan and flowtime. This algorithm generates pareto global optimal solutions for this bi-objective task scheduling problem. NSGA-II is implemented by using the set of benchmark instances. The experimental result shows NSGA-II generates efficient optimal schedules.
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...gerogepatton
This document describes a hybrid algorithm that combines the Invasive Weed Optimization Algorithm (IWO) and Grey Wolf Optimization Algorithm (GWO). IWO is inspired by the colonial behavior of invasive weeds, while GWO is based on the hunting behavior of grey wolves. The hybrid algorithm IWOGWO is proposed to take advantage of the strengths of both algorithms while minimizing their weaknesses. The document provides detailed descriptions of the IWO, GWO, and the hybridization process between them. It is argued that the hybrid algorithm finds the optimal solution in most test functions when compared to the original algorithms.
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...ijaia
In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO.Comparing the suggested hybrid algorithm with the original algorithms it results were excellent. The optimum solution was found in most of test functions.
This document summarizes a research article from the International Journal of Electronics and Communication Engineering & Technology. The article compares the performance of three genetic algorithm crossover operators - PMX, OX, and CX - for solving the Traveling Salesman Problem (TSP). It finds that the PMX operator enables achieving a better solution than the other two operators tested. The document provides background on genetic algorithms and describes the TSP optimization problem, literature on using genetic algorithms for TSP, and proposes a new PMX crossover scheme to resolve TSP more efficiently.
Non-convex constrained economic power dispatch with prohibited operating zone...IJECEIAES
This paper is focused on the solution of the non-convex economic power dispatch problem with piecewise quadratic cost functions and practical operation constraints of generation units. The constraints of the economic dispatch problem are power balance constraint, generation limits constraint, prohibited operating zones and transmission power losses. To solve this problem, a meta-heuristic optimization algorithm named crow search algorithm is proposed. A constraint handling technique is also implemented to satisfy the constraints effectively. For the verification of the effectiveness and the superiority of the proposed algorithm, it is tested on 6-unit, 10-unit and 15-unit test systems. The simulation results and statistical analysis show the efficiency of the proposed algorithm. Also, the results confirm the superiority and the high-quality solutions of the proposed algorithm when compared to the other reported algorithms.
This document discusses using particle swarm optimization based on variable neighborhood search (PSO-VNS) to attack classical cryptography ciphers. PSO is a population-based optimization algorithm inspired by bird flocking behavior. VNS is a metaheuristic algorithm that explores neighborhoods of solutions to escape local optima. The paper proposes improving PSO with VNS to find better solutions. It evaluates PSO-VNS on substitution and transposition ciphers, finding it recovers keys better than standard PSO and other variants.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
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Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
2. 52 Computer Science & Information Technology (CS & IT)
Some of the distinctive properties of IWO in comparison with other numerical search algorithms
are the way of reproduction, spatial dispersal, and competitive exclusion. These properties are
presented in details in section 3. Section 2 introduces and formulates the flexible job shop
scheduling problem .The experiments are provided in section 4. Finally, brief conclusions and
future perspectives are discussed in section 5.
2. MATHEMATICAL FORMULATION
The problem of flexible job shop scheduling (FJSSP) belongs to the NP-hard family [2]. It
presents two difficulties. The first one is the assignment of each operation to a machine, and the
second one is the scheduling of this set of operations in order to optimize our criteria. The result
of a scheduling algorithm must be a schedule that contains the start times and allocation of
resources to each operation. The data, constraints and objective of our problem are as follows:
2.1. Data
• M represents a set of m machines. A machine is called ( 1,..., )kM k m= , each kM has a
Workload called kW .
• N represents a set of n jobs. A job is called ( 1,... )ij i n= , each job has a linear sequence
of in operations.
• ,i jO represents the operation number j of the job number i . The realization of each
operation ,i jO requires a machine kM and a processing time , ,i j kp . The starting time of
,i jO is ,i jt and the ending time is ,i jft .
2.2. Constraints
• Machines are independent of one another.
• A machine can be unavailable during the scheduling (case of machine breakdown).
• Jobs are independent of one another.
• In our work, we suppose that: each job ij can start at the date 0t = and the total number
of operations to perform is greater than the number of machines.
2.3. Criteria
We have to minimize 1Cr , 2Cr and 3Cr :
• The makespan: 1Cr
• The total workload of machines: 2Cr
• The workload of the most loaded machine: 3Cr
In this paper, the objective is to find a schedule which has a minimum makespan, a minimum
total workload of machines and a minimum workload of the critical machine. The sum of these
three objectives is taken as the objective function. To measure the quality of solutions found, we
use the lower bounds ( 1BCr for makespan, 2BCr for total machine workload, and 3BCr for
the workload of the most loaded machine) proposed in [3].
3. Computer Science & Information Technology (CS & IT) 53
3. INVASIVE WEED OPTIMIZATION ALGORITHM FOR FJSSP
The IWO algorithm was proposed by Mehrabian and Lucas [1] in 2006, and since then, it has
been successfully utilized in different practical optimization problems such as optimal positioning
of piezoelectric actuators [4], demanding a recommender system [5], Studying electricity market
dynamics [6], Design of an E shaped MIMO antenna [7] and encoding sequences for DNA
computing [8].
3.1. Invasive Weed Optimization Algorithm
A weed is any plant growing where it is not wanted. Weeds have shown very robust and adaptive
nature which turns them to undesirable plants in agriculture. A common belief in agronomy is
that “The Weeds Always Win”. The harder people try, the better they get [1]. 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. The flow chart of this algorithm is shown in Figure1 and the
details of IWO are addressed as follows:
Figure 1. Flow Chart of IWO
3.1.1. Initialization
A population of initial solutions (weeds) is randomly generated over the search space.
4. 54 Computer Science & Information Technology (CS & IT)
3.1.2. Evaluation
The fitness of each weed in the population is calculated.
3.1.3. Reproduction
Each weed in the population is allowed to produce seeds depending on its comparative fitness in
the population. In other words, a weed will produce seeds based on its fitness, the worst fitness
and the best fitness in the population. In such way, the increase of number of seeds produced is
linear. The number of seeds for each weed varies linearly between minS for the worst plant and
maxS for the best plant. Figure 2 illustrates the procedure of reproduction.
Figure 2. Procedure of reproduction
The equation for determining numWeed the number of seeds produced by each weed is presented in
equation (1):
min max min( ) worst
num
best worst
f f
Weed S S S
f f
−
= + −
−
Equation (1)
Where f is the fitness of the weed considered, worstf and bestf are respectively the worst and the
best fitness in the population. For better clarification, the application of equation (1) is shown in
Figure 3. In this figure, it is assumed that weed5 and weed1 are the worst and best weeds between
a population containing five weeds. So, the number of seeds around Weed5 is equal to minS and the
number of seeds around Weed1 is equal to maxS .
5. Computer Science & Information Technology (CS & IT) 55
Figure 3. Schematic reproduction procedure for a problem with 5 weeds
3.1.4. Spatial Dispersal
This step ensures that the produced seeds will be generated around the parent weed, leading to a
local search around each plant. The generated seeds are randomly spread out around the parent
weeds according to a normal distribution with mean equal to zero and variance 2
σ . The standard
deviation of the seed dispersion σ decreases as a function of the number of iterationsiter . The
equation for determining the standard deviation for each generation is presented in equation (2):
max
max
( )
( )
( )
n
iter initial final finaln
iter iter
iter
σ σ σ σ
−
= − + Equation (2)
Where maxiter is the maximum number of iterations. iterσ is the standard deviation at the current
iteration and n is the nonlinear modulation index. Obviously, the value of σ defines the
exploration ability of the weeds. Therefore, as iter increases, the exploration ability of all weeds
is gradually reduced. At the end of the optimization process, the exploration ability has
diminished so much that every weed can only fine its position [9].
3.1.5. Competitive exclusion
After a number of iterations, the population reaches its maximum, and an elimination mechanism
is adopted: The seeds and their parents are ranked together and only those with better fitness can
survive and become reproductive. Others are being eliminated.
3.2. Weed representation of FJJSP
The original IWO is developed to solve continuous optimization problems, but it can not be
applied to discrete problems directly: individuals must be encoded appropriately to solve
scheduling problems. In this paper, we implement a coding that takes into account all the
constraints and the specifities of the problem. For the ( n jobs, m machines, O operations)
FJSSP, each plant is represented by four components: each component contains 2 O× number of
dimensions. Figure 5, Figure 6 and Figure 7 illustrate the solution representation of a weed
corresponding to (3 jobs, 5 machines, 8 operations) FJSSP described in Figure 4. The 1st
and
2nd
halves of the 1st
row of the weed (Figure 6 and Figure 7) represent operations as repetition of
jobs (Figure 5). For example ( 1J , 1J , 1J ) represents ( 1,1O , 1,2O , 1,3O ), ( 2J , 2J , 2J ) represents
( 2,1O , 2,2O , 2,3O ), and so on. The 2nd
row of (Figure 6 and Figure 7) represents weed’s position.
Each dimension of this row in Figure 6 maps one operation and each dimension of this row in
6. 56 Computer Science & Information Technology (CS & IT)
Figure 7 maps one machine. At this step, we use the Smallest Position Value (SPV) rule [10] to
find the permutation of jobs. The smallest component of the weed’s position in Figure 6 is -8
which corresponds to job number 1, thus 1J (or the first operation of 1J ) is scheduled first. The
second smallest component of the weed’s position is -5,2 which corresponds to job number 2,
therefore 2J (or the first operation of 2J )is the second in ordering, etc. The 2nd
row of Figure 6
contains a random number in the interval [0, ]m that indicates after being rounded to its nearest
integer the machine to which an operation is assigned during the course of IWO. The 3rd
row of
Figure 6 indicates the sequence of jobs in the ordering and the 3rd
row of Figure 7 indicates the
corresponding machines. Finally, the last row of Figure 6 indicates operations in the order and the
last row of Figure 7 indicates starting times. In conclusion, the weed itself presents a solution as it
shown in 3rd
and 4th
row of Figure 6 and Figure 7: First, the operation 1,1O of job 1J is executed
by the machine 1M at time 0t = , and then the operation 2,1O of job 2J is executed by the machine
1M at time 1t = , and so on.
1M 2M 3M 4M 5M
1,1O 1 9 3 7 5
1J 1,2O 3 5 2 6 4
1,3O 6 7 1 4 3
2,1O 1 4 5 3 8
2J 2,2O 2 8 4 9 3
2,3O 9 5 1 2 4
3J 3,1O 1 8 9 3 2
3,2O 5 9 2 4 3
Figure 4. Example of ( 3 J ,5 ,M 8 O ) FJSSP
Figure 5. Weed representation
7. Computer Science & Information Technology (CS & IT) 57
Figure 6. The first half of the weed Figure 7. The second half of the weed
3.3. Pseudo-code of solving FJSSP by IWO algorithm
Begin{
• Initialize population of weeds, set parameters;
• Current_iteration=1;
While (Current_iteration< Max_iteration)do
{
• Compute the best and worst fitness in the population
• Compute the standard deviation std depending on iteration
For each weed w in the population W
{
• Compute the number of seeds for w depending on its
fitness
• Select the seeds from the feasible solutions around the
parent weed w in a neighborhood with normal distribution
having mean=0 and standard deviation=std;
• Add seeds produced to the population W
If (|W|>Max_SizePopulation)
{
• Sort the population W according to their fitness
• W=SelectBetter(weed,seed,Max_SizePopulation)
}End if
}End for
Current_iteration=Current_iteration+1;
}End while
}End
Figure 8. Pseudo code of IWO
4. EXPERIMENTAL RESULTS
Our approach is implemented in C++ on an Intel(R) Core(TM) i3 CPU M370@2,40 GHz
machine. The non deterministic nature of IWO algorithm makes it necessary to carry out multiple
runs on the same problem instance in order to obtain meaningful results. We run our algorithm
twenty times from different starting solutions and tested it on a number of instances from
8. 58 Computer Science & Information Technology (CS & IT)
literature. The convergence of IWO depends on the selection of three parameters: the initial
standard deviation initialσ , the final standard deviation finalσ and the non linear modulation
index n .The chosen parameters for IWO are given in table 1.
Table 1. Parameters of IWO.
Parameters values
Number of initial population 50
Maximum number of population 200
Maximum number of iterations: maxiter 5000
Maximum number of seeds 5
Minimum number of seeds 1
initialσ 10
finalσ 0,5
Non linear modulation index: n 3
To illustrate the effectiveness and performance of the algorithm used in this paper, we choose
different instances of the problem of flexible job shop scheduling problem taken from Kacem
[11]. Solutions in the literature to the instances presented in table 2 are presented in table3.
Table 2. Instances of Kacem.
Instances n(jobs) m(machines)
Instance 1 3 5
Instance 2 4 5
Instance 3 10 7
Instance 4 10 10
Instance 5 15 10
Instance 6 8 8
From table 3, we conclude that the obtained solutions are generally of a good quality. This is
noted while comparing them with the existing approaches in the literature (for example Xia
approach[12]) and also while comparing obtained values of the criteria with the computed lower
bounds [3]. In fact, for instance 1, instance 2 and instance 3 our value of makespan 1Cr is near
the lower bound 1BCr , our value of total machine workload 2Cr is near the lower bound 2BCr
and our value of the workload of the critical machine 3Cr is near the lower bound 3BCr .
For instance 4, instance 5 and instance 6 our values of criteria are near lower bounds and similar
or better (instance 4) than solutions found in [12].
9. Computer Science & Information Technology (CS & IT) 59
Table 3. Solutions in Literature.
Instances Lower
Bounds
Xia et al
[12]
IWO
Instance 1
1BCr =4
2BCr =11
3BCr =2
-
-
-
1Cr =5
2Cr =13
3Cr =5
Instance 2
1BCr =11
2BCr =32
3BCr =6
-
-
-
1Cr =11
2Cr =32
3Cr =10
Instance 3
1BCr =11
2BCr =60
3BCr =8
-
-
-
1Cr =11
2Cr =61
3Cr =11
Instance 4
1BCr =7
2BCr =41
3BCr =4
1Cr =7
2Cr =44
3Cr =6
1Cr =7
2Cr =42
3Cr =6
Instance 5
1BCr =10
2BCr =91
3BCr =9
1Cr =12
2Cr =91
3Cr =11
1Cr =12
2Cr =91
3Cr =11
Instance 6
1BCr =12
2BCr =73
3BCr =9
1Cr =15
2Cr =75
3Cr =12
1Cr =14
2Cr =77
3Cr =12
5. CONCLUSIONS
In this paper, the performance of the Invasive Weed Optimization technique is investigated for
solving the multiobjective flexible job shop scheduling problem. The main highlighting features
in IWO are: it is simple and easy to understand and program and it has strong robustness and fast
global searching ability.
Experimental results are encouraging since that the proposed algorithm is able to find relevant
solutions minimizing makespan, total machine workload and the biggest machine workload on
the studied instances. A more comprehensive study on a large number of instances should be
made to test the efficiency of the proposed solution technique. Further investigation is needed to
fully reveal the ability of IWO in tackling scheduling problems and solving other optimization
problems. Future research should pay more attention to the hybridization of IWO and other
metaheuristics in order to benefit from advantages of each algorithm.
REFERENCES
[1] A R, Mehrabian. & C, Lucas, (2006) “A novel numerical optimization algorithm inspired from weed
colonization”, Ecological Informatics, Vol.1, pp355-366.
[2] M , Sakarovitch, (1984) “Optimisation combiantoire. Méthodes mathématiques et algorithmiques.
Hermann, Editeurs des sciences et des arts, Paris.
10. 60 Computer Science & Information Technology (CS & IT)
[3] R, Dupas, (2004) “ amelioration de performances des systems de production: apport des algorithms
évolutionnistes aux problems d’ordonnancement cycliques et flexibles, Habilitation , Artois
university.
[4] A R, Mehrabian & A, Yousefi-Koma (2007) “Optimal Positioning of Piezoelectric actuators on a
smart fin using bio-inspired algorithms”, Aerospace Science and technology, Vol 11, pp 174-182.
[5] H, Sepehri Rad & C, Lucas (2007) “ A recommender system based on invasive weed optimization
algorithm”, IEEE Congress on Evolutionary Computation, CEC 2007, pp 4297-4304.
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AUTHORS
Souad Mekni: received the diploma of Engineer in Computer Science from the Faculty of Science of
Tunis (Tunisia) in 2003 and the Master degree in Automatic and Signal Processing from the National
Engineering School of Tunis (Tunisia) in 2005. She is currently pursuing the Ph.D.degree in Electrical
Engineering at the National Engineering School of Tunis. Her research interests include production
scheduling, genetic algorithms, particle swarm optimization, multiobjective optimization, Invasive Weed
Optimization and artificial intelligence.
Besma Fayéch Chaâr: received the diploma of Engineer in Industrial Engineering from the National
Engineering School of Tunis (Tunisia) in 1999, the D.E.A degree and the Ph.D degree in Automatics and
Industrial Computing from the University of Lille (France), in 2000, 2003, respectively. Currently, she is
a teacher assistant in the Higher School of Sciences and Techniques of Tunis (Tunisia). Her research
interests include scheduling, genetic algorithms, transportation systems, multiagent systems and decision-
support systems.