Self-adaptive multi-objective differential evolution with direction information provided by archived inferior solutions

We propose a new self-adaptive differential evolution algorithm for multi-objective optimization problems. To address the challenges in multi-objective optimization, we introduce an archive to store recently explored inferior solutions whose difference with the current population is utilized as direction information about the optimum, and also consider a fairness measure in calculating crowding distances to prefer the solutions whose distances to nearest neighbors are large and close to be uniform. As a result, the obtained solutions can spread well over the computed non-dominated front and the front can be moved fast toward the Pareto-optimal front. In addition, the control parameters of the algorithm are adjusted in a self-adaptive manner, avoiding parameter tuning for prob lems of different characteristics. The proposed algorithm, named JADE2, achieves better or at least competitive results compared to NSGA-II and GDE3 for a set of twenty-two benchmark problems.