Particle Swarm Assisted Incremental Evolution Strategy for Function Optimization

Abstract

This paper presents a new evolutionary approach for function optimization problems particle swarm assisted incremental evolution strategy (PIES). Two strategies are proposed. One is incremental optimization that the whole evolution consists of several phases and one more variable is focused in each phase. The number of phases is equal to the number of variables in maximum. Each phase is composed of two stages: in the single-variable evolution (SVE) stage, a population is evolved with respect to one independent variable in a series of cutting planes; in the multi-variable evolving (MVE) stage, the initial population is formed by integrating the population obtained by the SVE in current phase and by the MVE in the last phase. And then the MVE is taken on the incremented variable set. The second strategy is a hybrid of particle swarm optimization (PSO) and the evolution strategy (ES). PSO is applied to adjust the cutting planes (in SVEs) or hyper-planes (in MVEs) while ES is applied to searching optima in the cutting planes/hyper-planes. The results of experiments show that PIES generally outperforms three other evolutionary algorithms, improved normal GA, PSO and SADEXERAF, in the sense that PIES finds solutions with more optimal objective values and closer to the true optima