Angel E. Munoz-Zavala

Autonomous University of Aguascalientes | UAA · Departamento de Estadística

A comparative study is performed to reveal the convergence characteristics and the robustness of three local neighborhoods in the particle swarm optimization algorithm (PSO): ring, Von Neumann and singly-linked ring. In the PSO algorithm, a neighborhood enables different communication paths among its members, and therefore, the way the swarm search...

Several systems can perform their intended functions at more than two different levels, from perfectly working to completely failed. These kind of systems are known as multi-state systems. In many complex and sophisticated systems, reliability and maintainability theory plays a very important role in maintaining such systems. The purpose of MSS mai...

The Vehicle Routing Problem with Time Windows (VRPTW), is an extension to the standard vehicle routing problem. VRPTW includes an additional constraint that restricts every customer to be served within a given time window. An approach for the VRPTW with the next three objectives is presented: 1)total distance (or time), 2)total waiting time, 3)numb...

The goal of the present work is to apply the computational properties of cultural technology, such as data mining and, to propose the solution of a real problem about society modeling: the Eurovision Song Contest. We analyze the voting behavior and ratings of judges using data mining techniques. The dataset makes it possible to analyze the determin...

This work introduces a hybrid PSO algorithm which includes perturbation operators to keep population diversity. A new neighborhood structure for Particle Swarm Optimization called Singly-Linked Ring is implemented. The approach proposes a neighborhood similar to the ring structure, but which has an innovative neighbors selection. The objective is t...

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 op...

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 tec...

Evolutionary computation is generic name given to the resolution of computational problems with base in models of an evolutionary process. Most of the evolutionary algorithms propose biological paradigms, and concepts of natural selection, mutation and reproduction. Nevertheless other paradigms exist which can be adopted in the creation of evolutio...

The intention of the present work is to apply data mining and PSO to propose the solution of a specific problem about society modelling. We analyze the voting behavior and ratings of judges in a popular song contest held every year in Europe. The dataset makes it possible to analyze the determinants of success, and gives a rare opportunity to run a...

In this chapter, we described a robust PSO for solving constrained optimization problems. We discussed the premature convergence problem, which still is an issue in evolutionary computation. A brief trip was made through several proposals to attain a balance between exploration and exploitation. Also, we briefly review recent works that contribute...

This paper introduces the COPSO algorithm (Constrained Optimization via Particle Swarm Opti- mization) for the solution of single objective constrained optimization problems. The approach includes two new perturbation operators to prevent premature convergence, and a new ring neighborhood struc- ture. A constraint handling technique based on feasib...

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...

This papers proposes an enhanced Particle Swarm Optimization algorithm with multi-objective optimization concepts to handle constraints, and operators to keep diversity and exploration. Our approach, PESDRO, is found robust at solving redundancy and reliability allocation problems with two objective functions: reliability and cost. The approach use...

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 O...