Hierarchic Genetic Strategy with maturing as a generic tool for Multiobjective Optimization

• Published in: Journal of computational science Vol. 17; pp. 249 - 260
• Main Authors: Łazarz, Radosław, Idzik, Michał, Gądek, Konrad, Gajda-Zagórska, Ewa
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2016
• Subjects: Adaptive metaheuristic; Hierarchic evolutionary algorithm; Multiobjective Optimization; Hierarchic evolutionary algorithm; Adaptive metaheuristic; Multiobjective Optimization
• ISSN: 1877-7503; 1877-7511
• DOI: 10.1016/j.jocs.2016.03.004

•Maturing modification to HGS and MO-HGS metaheuristics (MO-mHGS) is proposed.•MO-mHGS can be combined with several well-performing MOEAs and PSO-based methods.•MO-mHGS is compared with single-deme algorithms and with IMGA on a set of benchmarks.•MO-mHGS wins in early computation phase and in problems with time-accuracy dependency. In this paper we introduce the Multiobjective Optimization Hierarchic Genetic Strategy with maturing (MO-mHGS), a meta-algorithm that performs evolutionary optimization in a hierarchy of populations. The maturing mechanism improves growth and reduces redundancy. The performance of MO-mHGS with selected state-of-the-art multiobjective evolutionary algorithms as internal algorithms is analysed on benchmark problems and their modifications for which single fitness evaluation time depends on the solution accuracy. We compare the proposed algorithm with the Island Model Genetic Algorithm as well as with single-deme methods, and discuss the impact of internal algorithms on the MO-mHGS meta-algorithm.