Genetic Particle Swarm Optimization Based on Estimation of Distribution

Estimation of distribution algorithms sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. In this paper, a modified genetic particle swarm optimization algorithm based on estimation of distribution is propo...

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Published inBio-Inspired Computational Intelligence and Applications Vol. 4688; pp. 287 - 296
Main Author Wang, Jiahai
Format Book Chapter
LanguageEnglish
Published Germany Springer Berlin / Heidelberg 2007
Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Combinatorial Optimization Problem
Knapsack Problem
Particle Swarm
Particle Swarm Optimization
Probability Vector
Online AccessGet full text
ISBN3540747680
9783540747680
ISSN0302-9743
1611-3349
DOI10.1007/978-3-540-74769-7_32

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Abstract Estimation of distribution algorithms sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. In this paper, a modified genetic particle swarm optimization algorithm based on estimation of distribution is proposed for combinatorial optimization problems. The proposed algorithm incorporates the global statistical information collected from local best solution of all particles into the genetic particle swarm optimization. To demonstrate its performance, experiments are carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that the proposed algorithm has superior performance to other discrete particle swarm algorithms.
AbstractList Estimation of distribution algorithms sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. In this paper, a modified genetic particle swarm optimization algorithm based on estimation of distribution is proposed for combinatorial optimization problems. The proposed algorithm incorporates the global statistical information collected from local best solution of all particles into the genetic particle swarm optimization. To demonstrate its performance, experiments are carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that the proposed algorithm has superior performance to other discrete particle swarm algorithms.
Author Wang, Jiahai
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Snippet Estimation of distribution algorithms sample new solutions from a probability model which characterizes the distribution of promising solutions in the search...
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StartPage 287
SubjectTerms Combinatorial Optimization Problem
Knapsack Problem
Particle Swarm
Particle Swarm Optimization
Probability Vector
Title Genetic Particle Swarm Optimization Based on Estimation of Distribution
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