Research Area:  Metaheuristic Computing

Abstract:

The aim of evolutionary multi/many-objective optimization is to obtain a set of Pareto-optimal solutions with good trade-off among the multiple conflicting objectives. However, the convergence and diversity of multiobjective evolutionary algorithms often seriously decrease with the number of objectives and decision variables increasing. In this paper, we present a decomposition-based evolutionary algorithm for solving scalable multi/many-objective problems. The key features of the algorithm include the following three aspects: (1) a resource allocation strategy to coordinate the utility value of subproblems for good coverage; (2) a multioperator and multiparameter strategy to improve adaptability and diversity of the population; and (3) a bidirectional local search strategy to prevent the decrease in exploration capability during the early stage and increase the exploitation capability during the later stage of the search process. The performance of the proposed algorithm is benchmarked extensively on a set of scalable multi/many-objective optimization problems. The statistical comparisons with seven state-of-the-art algorithms verify the efficacy and potential of the proposed algorithm for scalable multi/many-objective problems.

Keywords:  
Multiobjective optimization
Many-objective optimization
Decomposition-based evolutionary algorithm
Resource allocation
Multioperator and multiparameter
Bidirectional local search

Author(s) Name:  Jiaxin Chen, Jinliang Ding, Kay Chen Tan & Qingda Chen

Publisher name:  Springer

DOI:  10.1007/s12293-021-00330-z

Volume Information:  volume 13, pages 413–432 (2021)

Paper Link:   https://link.springer.com/article/10.1007/s12293-021-00330-z