 A hybrid PSO-GA algorithm for constrained optimization problemsHarish Garg Applied Mathematics and Computation, 2016, vol. 274, issue C, 292-305 Abstract: The main objective of this paper is to present a hybrid technique named as a PSO-GA for solving the constrained optimization problems. In this algorithm, particle swarm optimization (PSO) operates in the direction of improving the vector while the genetic algorithm (GA) has been used for modifying the decision vectors using genetic operators. The balance between the exploration and exploitation abilities have been further improved by incorporating the genetic operators, namely, crossover and mutation in PSO algorithm. The constraints defined in the problem are handled with the help of the parameter-free penalty function. The experimental results of constrained optimization problems are reported and compared with the typical approaches exist in the literature. As shown, the solutions obtained by the proposed approach are superior to those of existing best solutions reported in the literature. Furthermore, experimental results indicate that the proposed approach may yield better solutions to engineering problems than those obtained by using current algorithms. Keywords: Particle swarm optimization; Genetic algorithm; Constraint optimization; PSO-GA (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:274:y:2016:i:c:p:292-305 DOI: 10.1016/j.amc.2015.11.001 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
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