Constrained optimization via particle evolutionary swarm optimization algorithm (PESO)

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-perturba tion". 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 wellknown benchmark for evolutionary constrained optimization. PESO matches most results and outperforms other PSO algorithms.

A particle swarm optimization algorithm based on an improved deb criterion for constrained optimization problems

“…The number of evaluations of the fitness function was in the CSHPSO algorithm ( Yadav & Deep, 2014 ). The number of evaluations of the fitness function was in the PESO algorithm ( Zavala, Aguirre & Diharce, 2005 ). The number of evaluations of the fitness function , , and in the PSO-DE algorithm ( Liu, Cai & Wang, 2010 ).…”

Evolutionary constrained optimization using an addition of ranking method and a percentage-based tolerance value adjustment scheme

“…We tested the algorithm on 13 benchmark problems [11,23] which have been widely adopted to validate the constraint handling techniques in EAs [12,15,16,18,25]. For further verification, we extended our performance evaluation to five additional optimization problems [14] and one engineering design problem [19].…”

A Garter Snake Optimization Algorithm for Constrained Optimization

“…This problem has been previously solved using various optimization algorithms, including HM (Koziel & Michalewicz, 1999), CDE (Huang et al, 2007), CULDE (Landa Becerra & Coello, 2006), CAEP (Coello Coello & Becerra, 2004), HPSO , PESO (Muñoz Zavala et al, 2005), ABC (Karaboga & Basturk, 2007a), SR (Runarsson & Yao, 2000), SMES (Mezura-Montes & Coello, 2005), and TLBO (Rao, Savsani, & Vakharia, 2011). Table 8 presents the statistical results of eleven optimizers, including the Garter Snake Optimization (GSO).…”