Locating potentially disjoint feasible regions of a search space with a particle swarm optimizer

Bonyadi, Mohammad Reza and Michalewicz, Zbigniew (2015). Locating potentially disjoint feasible regions of a search space with a particle swarm optimizer. In Rituparna Datta and Kalyanmoy Deb (Ed.), Evolutionary Constrained Optimization (pp. 205-230) India: Springer. doi:10.1007/978-81-322-2184-5_8


Author Bonyadi, Mohammad Reza
Michalewicz, Zbigniew
Title of chapter Locating potentially disjoint feasible regions of a search space with a particle swarm optimizer
Title of book Evolutionary Constrained Optimization
Place of Publication India
Publisher Springer
Publication Year 2015
Sub-type Research book chapter (original research)
DOI 10.1007/978-81-322-2184-5_8
Open Access Status Not Open Access
Series Infosys Science Foundation Series
ISBN 978-81-322-2183-8
978-81-322-2184-5
ISSN 2363-6149
Editor Rituparna Datta
Kalyanmoy Deb
Chapter number 8
Start page 205
End page 230
Total pages 26
Total chapters 10
Language eng
Formatted Abstract/Summary
In constraint optimization problems set in continuous spaces, a feasible search space may consist of many disjoint regions and the global optimal solution might be within any of them. Thus, locating these feasible regions (as many as possible, ideally all of them) is of great importance. In this chapter, we introduce niching techniques that have been studied in connection with multimodal optimization for locating feasible regions, rather than for finding different local optima. One of the successful niching techniques was based on the particle swarm optimizer (PSO) with a specific topology, called nonoverlapping topology, where the swarm was divided into several nonoverlapping sub-swarms. Earlier studies have shown that PSO with such nonoverlapping topology, with a small number of particles in each sub-swarm, is quite effective in locating different local optima if the number of dimensions is small (up to 8). However, its performance drops rapidly when the number of dimensions grows. First, a new PSO, called mutation linear PSO, MLPSO, is proposed. This algorithm is effective in locating different local optima when the number of dimensions grows. MLPSO is applied to optimization problems with up to 50 dimensions, and its results in locating different local optima are compared with earlier algorithms. Second, we incorporate a constraint handling technique into MLPSO; this variant is called EMLPSO. We test different topologies of EMLPSO and evaluate them in terms of locating feasible regions when they are applied to constraint optimization problems with up to 30 dimensions. The results of this test show that the new method with nonoverlapping topology with small swarm size in each sub-swarm performs better in terms of locating different feasible regions in comparison to other topologies, such as the global best topology and the ring topology.
Q-Index Code B1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Book Chapter
Collection: Centre for Advanced Imaging Publications
 
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Created: Wed, 13 Jul 2016, 07:06:12 EST by Reza Bonyadi on behalf of Centre for Advanced Imaging