This document proposes a Parallel Guided Local Search (PGLS) algorithm for continuous optimization problems. PGLS runs multiple Guided Local Search agents in parallel that periodically exchange information. The agents use local search and crossover to explore the search space. Preliminary experiments on benchmark functions show PGLS performs better than single-agent Guided Local Search by efficiently utilizing parallel computing resources and information exchange between agents.
EXPERIMENTS ON HYPOTHESIS "FUZZY K-MEANS IS BETTER THAN K-MEANS FOR CLUSTERING"IJDKP
Clustering is one of the data mining techniques that have been around to discover business intelligence by grouping objects into clusters using a similarity measure. Clustering is an unsupervised learning process that has many utilities in real time applications in the fields of marketing, biology, libraries, insurance, city-planning, earthquake studies and document clustering. Latent trends and relationships among data objects can be unearthed using clustering algorithms. Many clustering algorithms came into existence. However, the quality of clusters has to be given paramount importance. The quality objective is to achieve
highest similarity between objects of same cluster and lowest similarity between objects of different clusters. In this context, we studied two widely used clustering algorithms such as the K-Means and Fuzzy K-Means. K-Means is an exclusive clustering algorithm while the Fuzzy K-Means is an overlapping clustering algorithm. In this paper we prove the hypothesis “Fuzzy K-Means is better than K-Means for Clustering” through both literature and empirical study. We built a prototype application to demonstrate the differences between the two clustering algorithms. The experiments are made on diabetes dataset
obtained from the UCI repository. The empirical results reveal that the performance of Fuzzy K-Means is better than that of K-means in terms of quality or accuracy of clusters. Thus, our empirical study proved the hypothesis “Fuzzy K-Means is better than K-Means for Clustering”.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A HYBRID CLUSTERING ALGORITHM FOR DATA MININGcscpconf
Data clustering is a process of arranging similar data into groups. A clustering algorithm
partitions a data set into several groups such that the similarity within a group is better than
among groups. In this paper a hybrid clustering algorithm based on K-mean and K-harmonic
mean (KHM) is described. The proposed algorithm is tested on five different datasets. The research is focused on fast and accurate clustering. Its performance is compared with the traditional K-means & KHM algorithm. The result obtained from proposed hybrid algorithm is much better than the traditional K-mean & KHM algorithm
EXPERIMENTS ON HYPOTHESIS "FUZZY K-MEANS IS BETTER THAN K-MEANS FOR CLUSTERING"IJDKP
Clustering is one of the data mining techniques that have been around to discover business intelligence by grouping objects into clusters using a similarity measure. Clustering is an unsupervised learning process that has many utilities in real time applications in the fields of marketing, biology, libraries, insurance, city-planning, earthquake studies and document clustering. Latent trends and relationships among data objects can be unearthed using clustering algorithms. Many clustering algorithms came into existence. However, the quality of clusters has to be given paramount importance. The quality objective is to achieve
highest similarity between objects of same cluster and lowest similarity between objects of different clusters. In this context, we studied two widely used clustering algorithms such as the K-Means and Fuzzy K-Means. K-Means is an exclusive clustering algorithm while the Fuzzy K-Means is an overlapping clustering algorithm. In this paper we prove the hypothesis “Fuzzy K-Means is better than K-Means for Clustering” through both literature and empirical study. We built a prototype application to demonstrate the differences between the two clustering algorithms. The experiments are made on diabetes dataset
obtained from the UCI repository. The empirical results reveal that the performance of Fuzzy K-Means is better than that of K-means in terms of quality or accuracy of clusters. Thus, our empirical study proved the hypothesis “Fuzzy K-Means is better than K-Means for Clustering”.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A HYBRID CLUSTERING ALGORITHM FOR DATA MININGcscpconf
Data clustering is a process of arranging similar data into groups. A clustering algorithm
partitions a data set into several groups such that the similarity within a group is better than
among groups. In this paper a hybrid clustering algorithm based on K-mean and K-harmonic
mean (KHM) is described. The proposed algorithm is tested on five different datasets. The research is focused on fast and accurate clustering. Its performance is compared with the traditional K-means & KHM algorithm. The result obtained from proposed hybrid algorithm is much better than the traditional K-mean & KHM algorithm
Experimental study of Data clustering using k- Means and modified algorithmsIJDKP
The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease
and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in
exploratory data analysis. This paper presents results of the experimental study of different approaches to
k- Means clustering, thereby comparing results on different datasets using Original k-Means and other
modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance
measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and
execution time
A HYBRID K-HARMONIC MEANS WITH ABCCLUSTERING ALGORITHM USING AN OPTIMAL K VAL...IJCI JOURNAL
Large quantities of data are emerging every year and an accurate clustering algorithm is needed to derive
information from these data. K-means clustering algorithm is popular and simple, but has many limitations
like its sensitivity to initialization, provides local optimum solutions. K-harmonic means clustering is an
improved variant of K-means which is insensitive to the initialization of centroids, but still in some cases it
ends up with local optimum solutions. Clustering using Artificial Bee Colony (ABC) algorithm always gives
global optimum solutions. In this paper a new hybrid clustering algorithm (KHM-ABC) is presented by
combining both K-harmonic means and ABC algorithm to perform accurate clustering. Experimental
results indicate that the performance of the proposed algorithm is superior to the available algorithms in
terms of the quality of clusters.
A NEW APPROACH IN DYNAMIC TRAVELING SALESMAN PROBLEM: A HYBRID OF ANT COLONY ...ijmpict
Nowadays swarm intelligence-based algorithms are being used widely to optimize the dynamic traveling salesman problem (DTSP). In this paper, we have used mixed method of Ant Colony Optimization (AOC) and gradient descent to optimize DTSP which differs with ACO algorithm in evaporation rate and innovative data. This approach prevents premature convergence and scape from local optimum spots and also makes it possible to find better solutions for algorithm. In this paper, we’re going to offer gradient descent and ACO algorithm which in comparison to some former methods it shows that algorithm has significantly improved routes optimization.
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Optimal components assignment problem subject to system reliability, total lead-time, and total cost
constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy
membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the
presented problem. The optimal solution found by the proposed approach is characterized by maximum
reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different
examples taken from the literature to illustrate its efficiency in comparison with other previous methods
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Optimal components assignment problem subject to system reliability, total lead-time, and total cost constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the presented problem. The optimal solution found by the proposed approach is characterized by maximum reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different examples taken from the literature to illustrate its efficiency in comparison with other previous methods.
A survey on Efficient Enhanced K-Means Clustering Algorithmijsrd.com
Data mining is the process of using technology to identify patterns and prospects from large amount of information. In Data Mining, Clustering is an important research topic and wide range of unverified classification application. Clustering is technique which divides a data into meaningful groups. K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. In this paper, we present the comparison of different K-means clustering algorithms.
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.
Optimized sensor selection for control and fault tolerance of electromagnetic...ISA Interchange
This paper presents a systematic design framework for selecting the sensors in an optimized manner, simultaneously satisfying a set of given complex system control requirements, i.e. optimum and robust performance as well as fault tolerant control for high integrity systems. It is worth noting that optimum sensor selection in control system design is often a non-trivial task. Among all candidate sensor sets, the algorithm explores and separately optimizes system performance with all the feasible sensor sets in order to identify fallback options under single or multiple sensor faults. The proposed approach combines modern robust control design, fault tolerant control, multi-objective optimization and Monte Carlo techniques. Without loss of generality, it's efficacy is tested on an electromagnetic suspension system via appropriate realistic simulations.
Harmony Search for Multi-objective Optimization - SBRN 2012lucasmpavelski
Slides used in the presentation of the article "Harmony Search for Multi-objective
Optimization" in the 2012 Brazilian Symposium on Neural Networks (SBRN). Link to the article: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6374852
Experimental study of Data clustering using k- Means and modified algorithmsIJDKP
The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease
and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in
exploratory data analysis. This paper presents results of the experimental study of different approaches to
k- Means clustering, thereby comparing results on different datasets using Original k-Means and other
modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance
measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and
execution time
A HYBRID K-HARMONIC MEANS WITH ABCCLUSTERING ALGORITHM USING AN OPTIMAL K VAL...IJCI JOURNAL
Large quantities of data are emerging every year and an accurate clustering algorithm is needed to derive
information from these data. K-means clustering algorithm is popular and simple, but has many limitations
like its sensitivity to initialization, provides local optimum solutions. K-harmonic means clustering is an
improved variant of K-means which is insensitive to the initialization of centroids, but still in some cases it
ends up with local optimum solutions. Clustering using Artificial Bee Colony (ABC) algorithm always gives
global optimum solutions. In this paper a new hybrid clustering algorithm (KHM-ABC) is presented by
combining both K-harmonic means and ABC algorithm to perform accurate clustering. Experimental
results indicate that the performance of the proposed algorithm is superior to the available algorithms in
terms of the quality of clusters.
A NEW APPROACH IN DYNAMIC TRAVELING SALESMAN PROBLEM: A HYBRID OF ANT COLONY ...ijmpict
Nowadays swarm intelligence-based algorithms are being used widely to optimize the dynamic traveling salesman problem (DTSP). In this paper, we have used mixed method of Ant Colony Optimization (AOC) and gradient descent to optimize DTSP which differs with ACO algorithm in evaporation rate and innovative data. This approach prevents premature convergence and scape from local optimum spots and also makes it possible to find better solutions for algorithm. In this paper, we’re going to offer gradient descent and ACO algorithm which in comparison to some former methods it shows that algorithm has significantly improved routes optimization.
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Optimal components assignment problem subject to system reliability, total lead-time, and total cost
constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy
membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the
presented problem. The optimal solution found by the proposed approach is characterized by maximum
reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different
examples taken from the literature to illustrate its efficiency in comparison with other previous methods
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Optimal components assignment problem subject to system reliability, total lead-time, and total cost constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the presented problem. The optimal solution found by the proposed approach is characterized by maximum reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different examples taken from the literature to illustrate its efficiency in comparison with other previous methods.
A survey on Efficient Enhanced K-Means Clustering Algorithmijsrd.com
Data mining is the process of using technology to identify patterns and prospects from large amount of information. In Data Mining, Clustering is an important research topic and wide range of unverified classification application. Clustering is technique which divides a data into meaningful groups. K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. In this paper, we present the comparison of different K-means clustering algorithms.
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.
Optimized sensor selection for control and fault tolerance of electromagnetic...ISA Interchange
This paper presents a systematic design framework for selecting the sensors in an optimized manner, simultaneously satisfying a set of given complex system control requirements, i.e. optimum and robust performance as well as fault tolerant control for high integrity systems. It is worth noting that optimum sensor selection in control system design is often a non-trivial task. Among all candidate sensor sets, the algorithm explores and separately optimizes system performance with all the feasible sensor sets in order to identify fallback options under single or multiple sensor faults. The proposed approach combines modern robust control design, fault tolerant control, multi-objective optimization and Monte Carlo techniques. Without loss of generality, it's efficacy is tested on an electromagnetic suspension system via appropriate realistic simulations.
Harmony Search for Multi-objective Optimization - SBRN 2012lucasmpavelski
Slides used in the presentation of the article "Harmony Search for Multi-objective
Optimization" in the 2012 Brazilian Symposium on Neural Networks (SBRN). Link to the article: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6374852
Rapport projet de fin d'études: Elaboration d’un tableau de bord et politique...Ayoub Minen
Bonjour,
Voici le rapport pfe qui traite comme sujet l’élaboration d’un tableau de bord et politique d’approvisionnement Min-Max pour le magasin général PMP (PAKISTAN MAROC PHOSPHORE, OCP Jorf Lasfar)
This study introduces and compares different methods for estimating the two parameters of generalized logarithmic series distribution. These methods are the cuckoo search optimization, maximum likelihood estimation, and method of moments algorithms. All the required derivations and basic steps of each algorithm are explained. The applications for these algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50, 100). Results are compared using the statistical measure mean square error.
USING CUCKOO ALGORITHM FOR ESTIMATING TWO GLSD PARAMETERS AND COMPARING IT WI...ijcsit
This study introduces and compares different methods for estimating the two parameters of generalized logarithmic series distribution. These methods are the cuckoo search optimization, maximum likelihood estimation, and method of moments algorithms. All the required derivations and basic steps of each algorithm are explained. The applications for these algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50, 100). Results are compared using the statistical measure mean square error.
This study introduces and compares different methods for estimating the two parameters of generalized logarithmic series distribution. These methods are the cuckoo search optimization, maximum likelihood estimation, and method of moments algorithms. All the required derivations and basic steps of each algorithm are explained. The applications for these algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50, 100). Results are compared using the statistical measure mean square error.
Particle Swarm Optimization based K-Prototype Clustering Algorithm iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
COMPARING THE CUCKOO ALGORITHM WITH OTHER ALGORITHMS FOR ESTIMATING TWO GLSD ...csandit
This study introduces and compares different methods for estimating the two parameters of
generalized logarithmic series distribution. These methods are the cuckoo search optimization,
maximum likelihood estimation, and method of moments algorithms. All the required
derivations and basic steps of each algorithm are explained. The applications for these
algorithms are implemented through simulations using different sample sizes (n = 15, 25, 50,
100). Results are compared using the statistical measure mean square error.
Optimal rule set generation using pso algorithmcsandit
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
EFFECTS OF THE DIFFERENT MIGRATION PERIODS ON PARALLEL MULTI-SWARM PSOcscpconf
In recent years, there has been an increasing interest in parallel computing. In parallel computing, multiple computing resources are used simultaneously in solving a problem. There are multiple processors that will work concurrently and the program is divided into different tasks to be simultaneously solved. Recently, a considerable literature has grown up around the theme of metaheuristic algorithms. Particle swarm optimization (PSO) algorithm is a popular metaheuristic algorithm. The parallel comprehensive learning particle swarm optimization (PCLPSO) algorithm based on PSO has multiple swarms based on the master-slave paradigm and works cooperatively and concurrently. The migration period is an important parameter in PCLPSO and affects the efficiency of the algorithm. We used the well-known benchmark functions in the experiments and analysed the performance of PCLPSO using different migration periods.
Effects of The Different Migration Periods on Parallel Multi-Swarm PSO csandit
In recent years, there has been an increasing inter
est in parallel computing. In parallel
computing, multiple computing resources are used si
multaneously in solving a problem. There
are multiple processors that will work concurrently
and the program is divided into different
tasks to be simultaneously solved. Recently, a cons
iderable literature has grown up around the
theme of metaheuristic algorithms. Particle swarm o
ptimization (PSO) algorithm is a popular
metaheuristic algorithm. The parallel comprehensive
learning particle swarm optimization
(PCLPSO) algorithm based on PSO has multiple swarms
based on the master-slave paradigm
and works cooperatively and concurrently. The migra
tion period is an important parameter in
PCLPSO and affects the efficiency of the algorithm.
We used the well-known benchmark
functions in the experiments and analysed the perfo
rmance of PCLPSO using different
migration periods.
A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTIONijsc
Two modern optimization methods including Particle Swarm Optimization and Differential Evolution are
compared on twelve constrained nonlinear test functions. Generally, the results show that Differential
Evolution is better than Particle Swarm Optimization in terms of high-quality solutions, running time and
robustness.
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...cscpconf
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
An Improved Adaptive Multi-Objective Particle Swarm Optimization for Disassem...IJRESJOURNAL
With the development of productivity and the fast growth of the economy, environmental pollution, resource utilization and low product recovery rate have emerged subsequently, so more and more attention has been paid to the recycling and reuse of products. However, since the complexity of disassembly line balancing problem (DLBP) increases with the number of parts in the product, finding the optimal balance is computationally intensive. In order to improve the computational ability of particle swarm optimization (PSO) algorithm in solving DLBP, this paper proposed an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm. Firstly, the evolution factor parameter is introduced to judge the state of evolution using the idea of fuzzy classification and then the feedback information from evolutionary environment is served in adjusting inertia weight, acceleration coefficients dynamically. Finally, a dimensional learning strategy based on information entropy is used in which each learning object is uncertain. The results from testing in using series of instances with different size verify the effect of proposed algorithm.
USING LEARNING AUTOMATA AND GENETIC ALGORITHMS TO IMPROVE THE QUALITY OF SERV...IJCSEA Journal
A hybrid learning automata–genetic algorithm (HLGA) is proposed to solve QoS routing optimization problem of next generation networks. The algorithm complements the advantages of the learning Automato Algorithm(LA) and Genetic Algorithm(GA). It firstly uses the good global search capability of LA to generate initial population needed by GA, then it uses GA to improve the Quality of Service(QoS) and acquiring the optimization tree through new algorithms for crossover and mutation operators which are an NP–Complete problem. In the proposed algorithm, the connectivity matrix of edges is used for genotype representation. Some novel heuristics are also proposed for mutation, crossover, and creation of random individuals. We evaluate the performance and efficiency of the proposed HLGA-based algorithm in comparison with other existing heuristic and GA-based algorithms by the result of simulation. Simulation results demonstrate that this paper proposed algorithm not only has the fast calculating speed and high accuracy but also can improve the efficiency in Next Generation Networks QoS routing. The proposed algorithm has overcome all of the previous algorithms in the literature..
A Comparison of Particle Swarm Optimization and Differential Evolutionijsc
Two modern optimization methods including Particle Swarm Optimization and Differential Evolution are compared on twelve constrained nonlinear test functions. Generally, the results show that Differential Evolution is better than Particle Swarm Optimization in terms of high-quality solutions, running time and robustness.
Similar to Parallel Guided Local Search and Some Preliminary Experimental Results for Continuous Optimization (20)
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
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Computer Science & Information Technology (CS & IT)
information, learned from the previous search about the problem, with each other periodically for
speeding up the search. Some preliminary experiments have been carried out to study the
effectiveness of the proposed PGLS up the search.
The rest of the paper is organized as follows: the proposed PGLS is described in Section 2.
Sections 3 and 4 list the test instances and report experimental results. Section 5 concludes this
paper.
2. PARALLEL GUIDED LOCAL SEARCH
2.1. Guided Local Search
Guided Local Search (GLS) is a very general strategy for guiding a local search algorithm to
escape from local optima. In GLS, solution features are defined to distinguish between solutions
with different characteristics, so that bad characteristics can be penalized and hopefully removed.
The solution features depend on the problem and the local search. Features can be defined in a
very natural way. For example, in the travelling salesman problem (TSP), a feature can be a link
between two cities. When the search is trapped in a local optimum, GLS will use the following
function as the objective function:
hሺsሻ = gሺsሻ + λ ∑൫p୧ × I୧ ሺsሻ൯,
(1)
where sis a candidate solution and g(s) is the original objective to minimize, λ is a control
parameter, I ranges over the features. p୧ is the penalty for feature i. I i (s ) indicates whether or
not solution s exhibits feature i:
1
Ii (s) =
0
if s exhibits feature i ;
otherwise.
All the p i are initialized to be 0. When the local search traps at a local optimum s * , GLS
computes the utility function of penalizing feature i:
util୧ ሺs ∗ ሻ = I୧ ሺs ∗ ሻ ×
ୡ
ଵା୮
(2)
Where c୧ is the cost for the feature i (in TSP, the cost for a feature is the distance for that
feature), p୧ is the current penalty value forfeature୧. GLS penalizes the feature with the highest
utility value, i.e., increase its p୧ value by 1. The LS will then continue with the new augmented
objective function.
2.2. PGLS
In PGLS, several agents run GLS in a parallel way and exchange information periodically for
speeding up the search. Different agents start their searches from different points in the solution
*
space. PGLS (manager) records the best solution x found so far to the problem under
consideration by these agents. After every K penalizations, each agent conducts a crossover
operator on x * and its current solution to generate a new solution and then performs its search
3. Computer Science & Information Technology (CS & IT)
423
from this new solution. The p୧ values for each agent will be reset to zero. The algorithm with n
agents works as follows:
Step1: Initialization
Generate randomly n points x1 , K , x n in the search space.
Step2: Guided Local Search
2.1 For i=1 to n
Agent i does GLS from ݔ until K penalizations have been made and
returns its best solution ݖ found so farand its current solutionݕ .
2.2
Set ݖto be the best solution to the problem among all theݖ ’s.
Step 3
3.1 If the stopping condition is met, return .ݖ
3.2 Otherwise,
3.2.1 For i=1 to n, do crossover on ݖand ݕ to produce a new
solution
ݔ .
3.2.2 Go to Step 2.
2.2.1. Local Search
Following Voudris [9], each real-valued variable in a continuous optimization problem is
represented by a number of bits. Therefore, the flip bit mutation is used as a local search method.
The local search moves from one solution to another by flipping the value of a bit in the solution.
It starts from a random bit and then examines all the possible bits.
2.2.2. Crossover
In the crossover, 2n crossover sites are first randomly selected under the constraint that there are
two crossover sites in each variable substring, and then 2 child solutions are generated by
swapping along these sites. The child solution with lower original objective function value is
returned as a new solution (Step 3.2.1).
3. EXPERIMENTAL STUDIES
We have tested the above PGLS algorithm to minimize Rastriging function, Rosenbrock function,
and Schwefel function. These three functions are:
୬
ଶ
fୖୟ = 10n + ቀx୨ − 10 cos൫2πx ୨൯ቁ
୨ୀଵ
f୫୧୬ = 0, ൣx ୨ = 0൧,
୬ିଵ
ଶ
−5.12 ≤ x୨ ≤ 5.12
ଶ
ଶ
fୖ୭ = ቂ100൫x୨ାଵ − x ୨ ൯ + ൫x୨ − 1൯ ቃ
୨ୀଵ
ሺ3ሻ
ሺ4ሻ
f୫୧୬ = 0, ൣx୨ = 1൧, −2.048 ≤ x୨ ≤ 2.048
୬
fୗ୦ = 418.9829n − ቆx୨ sinටหx୨ หቇ
୨ୀଵ
ሺ5ሻ
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Computer Science & Information Technology (CS & IT)
f୫୧୬ = 0, ൣx୨ = 1൧, −500 ≤ x୨ ≤ 500
In our experiments, each real-valued variable is represented in 22 bits as in [9]. Therefore, each
solution is represented in 22n bits when n is the number of variables.
The GLS used in our experiments is the same as in [9]. The number of agents=2. The value of K
varies based on the problem as it is shown in Table 1. The algorithm stops after each agent has
done 100× K penalizations.
3. RESULTS
The obtained results show that the proposed cooperative method, PGLS, was the most effective
algorithm of the two algorithms. It showed a good performance and produced good results for the
lunched functions in every run. Table 1 shows the experimental results for the run algorithms
Table 1. Comparison between the run algorithms
4. CONCLUSIONS
A framework called PGLS for solving continuous optimization problems has been proposed. The
idea is to build a cooperative learning environment through running a number of agents of GLS to
efficiency explore the search space. After predetermined iterations the acquired information
through the search is exchanged between the agents to speed up the search. The genetic crossover
was exploited as an exchanging information operator between the agents. In order to allow
different components (variables) from the continuous spaces to be selected we apply the
crossover operation in each variable substring. Our experimental results on test continuous
optimization problems show that our method is the most effective and performed better than the
other mean of parallel –GLS: no coordination. Our future work is to expand the experiments with
more number of agents and variables. Furthermore, theoretical analysis for the proposed
algorithm should be conducted in order to study its convergence and computation time and thus to
perform further improvement to the algorithm and subsequently make comparisons with others
algorithms.
5. Computer Science & Information Technology (CS & IT)
425
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
R. Horst, N.V. Thoai and P.M. Pardalos, Introduction to Global Optimization, Second Edition,
Kluwer Academic Publishers, Boston, MA, 2000.
Boston
P.M. Pardalos and H. E. Romeijn (Eds.), Handbook of Global Optimization, Kluwer Academic
Publishers, Boston, MA, 2002.
Kirkpatrick, S., C. D. Gelatt Jr., M. P. Vecchi, "Optimization by Simulated Annealing",Science, 220,
4598, 671-680, 1983.
Fred Glover , Fred Laguna, Tabu Search , Kluwer Academic Publishers, Norwell, MA, 1997.
Voudouris, C, Guided local search for combinatorial optimization problems, PhD Thesis, Department
of Computer Science, University of Essex, Colchester, UK, July, 1997.
Colch
Hiroyasu, T., Miki, M. and Ogura M., “Parallel simulated annealing using genetic crossover”,
Proceedings of the IASTED International Conference on Parallel and Distributed Computing
Systems, Las Vegas, pp. 145-150, 2000.
150,
El-Abd, M. and Kamel, M., “A Taxonomy of Cooperative Search Algorithms”. Hybrid Meta
bd,
Metaheuristics (Springer Berlin - Lectures in Computer science), pp. 32-41, 2005.
32
Blum, C. and Roli, A. “Metaheuristics in Combinatorial Optimization: Overview and Conceptual
Comparison”. ACM Computing Surveys, vol. 35, no. 3, pp. 268- 308, 2003.
268
Voudouris, C., Guided Local Search -- An illustrative example in function optimisation, BT
Technology Journal, Vol.16, No.3, July 1998, 46-50.
46
AUTHORS
niversity,
1999,
Nasser Tairan received B.Sc. degree in computer science from King Abdulaziz University, KSA in 1999
M.Sc. degree in Software Engineering from University of Bradford, UK in 2005, M.Sc. degree in Computer
Science (AI) from University of Essex, UK in 2007 and P.hD. degree from University of Essex 2012. He is
currently Assistance Professor in King Khalid University, College of Computer Science, KSA. His main
University,
research areas are evolutionary computation, single and multiobjective optimization and metaheuristics,
Muhammad Asif Jan received the M.Sc. degree in mathematics from University of
Pehawar, Khyber Paktunkhwa, Pakistan in 1997 and the Ph.D. in mathematics from
University of Essex, UK, in 2011. He is currently the Chairperson of the Department
of Mathematics, Kohat Uiversity of Science & Technology (KUST), Khyber
Paktunkhwa, Pakistan. His main research areas are evolutionary computation,
researc
unconstrained/constrained single/multi- objective optimization by using evolutionary
single/multi
algorithms, decomposition based evolutionary methods for constrained multimulti
objective optimization, mathematical programming, and numerical analysis.
nume
Rashida Adeeb Khanum received the M.Sc. degree from University of Peshawar,
Pakistan in 1997and the Ph.D. from University of Essex, Colchester, United Kingdom
in 2012.