A Memetic Algorithm with Genetic Particle Swarm Optimization and Neural Network for Maximum Cut Problems

In this paper, we incorporate a chaotic discrete Hopfield neural network (CDHNN), as a local search scheme, into a genetic particle swarm optimization (GPSO) and develop a memetic algorithm GPSO-CDHNN for the maximum cut problem. The proposed algorithm not only performs exploration by using the popu...

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Bibliographic Details
Published inBio-Inspired Computational Intelligence and Applications Vol. 4688; pp. 297 - 306
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
Continuous Optimization Problem
Local Search Scheme
Maximum Clique Problem
Memetic Algorithm
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ISBN3540747680
9783540747680
ISSN0302-9743
1611-3349
DOI10.1007/978-3-540-74769-7_33

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Summary:In this paper, we incorporate a chaotic discrete Hopfield neural network (CDHNN), as a local search scheme, into a genetic particle swarm optimization (GPSO) and develop a memetic algorithm GPSO-CDHNN for the maximum cut problem. The proposed algorithm not only performs exploration by using the population-based evolutionary search ability of the GPSO, but also performs exploitation by using the CDHNN. Simulation results show that the proposed algorithm has superior ability for maximum cut problems.
ISBN:3540747680
9783540747680
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-540-74769-7_33