Genetic Particle Swarm Optimization Based on Estimation of Distribution

• Published in: Bio-Inspired Computational Intelligence and Applications Vol. 4688; pp. 287 - 296
• Main Author: Wang, Jiahai
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2007; Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Combinatorial Optimization Problem; Knapsack Problem; Particle Swarm; Particle Swarm Optimization; Probability Vector
• ISBN: 3540747680; 9783540747680
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-74769-7_32

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.