Self-organizing multiobjective optimization based on decomposition with neighborhood ensemble

Currently, most of the multiobjective evolutionary algorithms (MOEAs) directly adopt the reproduction operators designed for the single-objective optimization. Since these operators do not consider the characteristics of multiobjective optimization problems (MOPs), they cannot always perform well in the MOEAs. Inspired by this case, this paper presents a self-organizing reproduction mechanism based on the regularity property of MOPs, and proposes a self-organizing multiobjective evolutionary algorithm based on decomposition with neighborhood ensemble. In the new reproduction, a self-organizing map approach is firstly employed to discover the population distribution structure, and to build a mating pool for each solution. Thereafter, reproductions are only allowed among the solutions within the same mating pools. In order to establish the mating pools, an ensemble of multiple neuron neighborhood sizes is also introduced. The probability of choosing different neighborhood sizes is updated based on their performance on producing new solutions over the last certain generations. Comprehensive experiments denote that the proposed algorithm is efficient and competitive. The contributions of the new reproduction mechanism and neighborhood ensemble are also experimentally validated.