Mutation strategies toward Pareto front for multi-objective differential evolution algorithm

This paper presents a multi-objective differential evolution algorithm, called MODE, to search for a set of non-dominated solutions on the Pareto front. During the iterative search process, the non-dominated solutions found are stored as the 'Elite group' of solutions. The study focuses on utilising the solutions in the Elite group to guide the movement of the search. Several potential mutation strategies in MODE framework are proposed as the movement guidance in order to obtain the high-quality front. Each mutation strategy possesses distinct search behaviour which directs a vector in the DE population in different ways with the purpose of reaching the Pareto optimal front. The performance of the proposed algorithm is evaluated on a set of well-known benchmark problems and compared with results from other existing approaches. The experimental results demonstrate that the proposed MODE algorithm is a highly competitive approach for solving multi-objective optimisation problems.