Article

Constrained optimization via particle evolutionary swarm optimization algorithm (PESO)

Authors:

Angel E. Muñoz Zavala,

Arturo Hernández Aguirre,

Enrique R. Villa DiharceAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

Pages 209 - 216

https://doi.org/10.1145/1068009.1068041

Published: 25 June 2005 Publication History

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Abstract

We introduce the PESO (Particle Evolutionary Swarm Optimization) algorithm for solving single objective constrained optimization problems. PESO algorithm proposes two new perturbation operators: "c-perturbation" and "m-perturbation". The goal of these operators is to fight premature convergence and poor diversity issues observed in Particle Swarm Optimization (PSO) implementations. Constraint handling is based on simple feasibility rules. PESO is compared with respect to a highly competitive technique representative of the state-of-the-art in the area using a well-known benchmark for evolutionary constrained optimization. PESO matches most results and outperforms other PSO algorithms.

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Constrained optimization via particle evolutionary swarm optimization algorithm (PESO)

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Published In

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

June 2005

2272 pages

ISBN:1595930108

DOI:10.1145/1068009

Editor:
Hans-Georg Beyer
Vorarlberg University of Applied Sciences, Austria
,
General Chair:
Una-May O'Reilly
CSAIL, MIT

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 25 June 2005

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GECCO05: Genetic and Evolutionary Computation Conference

June 25 - 29, 2005

Washington DC, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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https://doi.org/10.1007/978-981-99-2832-3_65
Muñoz-Zavala AMacías-Díaz JAlba-Cuéllar DGuerrero-Díaz-de-León J(2024)A Literature Review on Some Trends in Artificial Neural Networks for Modeling and Simulation with Time SeriesAlgorithms10.3390/a1702007617:2(76)Online publication date: 7-Feb-2024
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Chaudhary ABhushan B(2023)An improved teaching learning based optimization method to enrich the flight control of a helicopter systemSādhanā10.1007/s12046-023-02271-448:4Online publication date: 21-Oct-2023
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Solving constrained optimization via a modified genetic particle swarm optimization

Self-adaptive velocity particle swarm optimization for solving constrained optimization problems

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