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.

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This paper introduces a new neighborhood structure for Particle Swarm Optimization, called Singly-Linked Ring. The approach proposes a neighborhood whose members share the information at a different rate. The objective is to avoid the premature convergence of the flock and stagnation into local optimal. The approach is applied at a set of global optimization problems commonly used in the literature. The singly-linked structure is compared against the state-of-theart neighborhoods structures. The proposal is easy to implement, and its results and its convergence performance are better than other structures.Track: Ant Colony Optimization and Swarm Intelligence.

Purpose -The purpose of this paper is to present a new constrained optimization algorithm based on a particle swarm optimization (PSO) algorithm approach. Design/methodology/approach -This paper introduces a hybrid approach based on a modified ring neighborhood with two new perturbation operators designed to keep diversity. A constraint handling technique based on feasibility and sum of constraints violation is adopted. Also, a special technique to handle equality constraints is proposed. Findings -The paper shows that it is possible to improve PSO and keeping the advantages of its social interaction through a simple idea: perturbing the PSO memory.Research limitations/implications -The proposed algorithm shows a competitive performance against the state-of-the-art constrained optimization algorithms. Practical implications -The proposed algorithm can be used to solve single objective problems with linear or non-linear functions, and subject to both equality and inequality constraints which can be linear and non-linear. In this paper, it is applied to various engineering design problems, and for the solution of state-of-the-art benchmark problems. Originality/value -A new neighborhood structure for PSO algorithm is presented. Two perturbation operators to improve PSO algorithm are proposed. A special technique to handle equality constraints is proposed.

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 "mperturbation". 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 mosts results and outperforms other PSO algorithms.