Reference-inspired many-objective evolutionary algorithm based on decomposition

Keeping balance between convergence and diversity for many-objective optimisation problems (having four or more objectives) is a very difficult task as revealed in existing research in multiobjective evolutionary optimisation. In this paper, we propose a reference-inspired multiobjective evolutionary algorithm for many-objective optimisation. The main idea is (1) to summarise information inspired by a set of randomly generated reference points in the objective space to strengthen the selection pressure towards the Pareto front; and (2) to decompose the objective space into subregions for diversity management and recombination. We showed that the mutual relationship between a population of solution and the reference points provides not only a new dominance relation to producing fine selection pressure but also a balanced convergence-diversity information that is able to adapt search dynamics. The partition of the objective space into several subregions is able to preserve the Pareto front’s diversity. Moreover, a restricted stable match strategy is proposed to choose appropriate parent solutions from solution sets constructed at the subregions for high-quality offspring generation. Controlled experiments conducted on commonly used benchmark test suites have shown the effectiveness and competitiveness of the proposed algorithm compared with several state-of-the-art many-objective evolutionary algorithms.