We introduce the PESO (Particle Evolutionary Swarm Optimization) algorithm for solving single objective constrained optimization problems. PESO algorithm, proposes two perturbation operators: "c-perturbation" and "m-perturbation", The goal of these operators is to prevent premature convergence and the poor diversity issues observed in Particle Swarm Optimization (PSO) implementations. Constraint handling is based on simple feasibility rules, enhanced with a dynamic e-tolerance approach applicable to equality constraints. PESO is compared and outperforms highly competitive algorithms representative of the state of the art. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Muñoz Zavala, A. E., Aguirre, A. H., & Villa Diharce, E. R. (2005). Particle evolutionary swarm optimization with linearly decreasing ε-tolerance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3789 LNAI, pp. 641–651). https://doi.org/10.1007/11579427_65
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