Multiple-Population Genetic Algorithm for Solving Min-Max Optimization Problems

Mohammad Alshraideh; Luay Tahat

doi:10.15866/irecos.v10i1.4612

Multiple-Population Genetic Algorithm for Solving Min-Max Optimization Problems

Mohammad Alshraideh⁽¹⁾, Luay Tahat^(2*)

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/irecos.v10i1.4612

Abstract

A min-max optimization problem was originally designed for simultaneous maximization of the same object functions during the same optimization run. The existing approaches for solving min-max problem using genetic algorithms is mainly focused on maintaining a single-population of candidate tests. In this paper, we explore a new approach for using genetic algorithms (GAs) to solve min-max problems. The approach uses a two-population GA to find Maximum and Minimum goals of separate search processes using distinct island populations. The advantage of the suggested approach is that its ability to explore a greater variety of execution paths increases the search efficiency under certain conditions. By applying this to a collection of benchmarks problems, it has been shown experimentally that the proposed multiple-population algorithm out performs the single-population algorithm in terms of the number of executions, execution time, performance improvement, and efficiency.
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Keywords

Single-Population; Multiple-Population; Genetic Algorithm; Island; Min-Max Problems

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