Optimization with a novel hybrid algorithm and applications

Abstract

A novel hybrid algorithm (DPD) by combination of Differential Evolution (DE) and Particle Swarm Optimization (PSO) is proposed for solving unconstrained and constrained optimization problems. It is based on ‘tri-population’ environment. Initially, the whole population (in increasing order of fitness) is divided into three groups—Inferior Group, Mid Group and Superior Group. DE is employed in the inferior and superior groups, whereas PSO is used in the mid-group. Two strategies namely Elitism (to retain the best obtained values so far) and Non-redundant search (to maintain diversity in the population) have been employed in DPD cycle. Since the performance of DE is sensitive to the choice of the mutation strategy. Therefore suitable mutation strategy for both DEs used in DPD is investigated over a set of 8 popular mutation strategies. Combination of 8 mutation strategies is generated 64 different variants of DPD. Top 4 DPDs are investigated through 20 benchmark function. Based on the ‘performance’ analysis best DPD is reported using 20 unconstrained benchmark functions, 13 constrained benchmark functions, 6 unconstrained and 3 constrained engineering design problems. To show superiority and effectiveness, best DPD is compared with various state-of-the-art approaches in terms of quality of solutions.