Design and applications of a new DE-PSO-DE algorithm for unconstrained optimisation problems

Abstract

This paper introduces a novel hybridisation between differential evolution (DE) and particle swarm optimisation (PSO), 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. It is based on the basic qualities of DE and PSO along with their information sharing mechanism. This proposed method is named as DPD as it uses DE-PSO-DE on a population. Two more strategies namely elitism (to retain the best obtained values so far) and non-redundant search (to improve the solution quality) have been incorporated in DPD cycle. Out of eight variants of mutation strategies for each of both DEs in DPD, the best combination is investigated. In other words, among 8 × 8 = 64 variants of DPDs, the top four DPDs where DEs use their best mutation strategies have been pointed out. Later they are being employed to solve a total of 13 unconstrained benchmark function and six real life problems. The results of top four DPDs compared with state-of-the-art algorithms. Finally the best DPD is recommended in the conclusion.