Particle Swarm Optimization with Adaptive Inertia Weight using Fuzzy Logic for Large-Scale Problems

In this paper an alternative approach is proposed to improve the convergence of Particle Swarm Optimization (PSO) algorithm by adapting the inertial weight parameter with a fuzzy logic system to solve large-scale optimization problems. The PSO algorithm is a population-based metaheuristic inspired by the social behavior of birds, and it has been applied to numerous optimization problems successfully. However, one of its main disadvantages is the decaying performance when applied to complex and large-scale problems. The proposed algorithm uses the fuzzy system to dynamically calculate a value of the Inertia Weight parameter during the search process to find better solutions. After carrying out experiments on a well-known benchmark for large-scale optimization, the proposed approach provides a competitive performance.Workshop: WASI – Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informátic

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field