Elite-recombination-based hybrid multi-objective evolutionary algorithm

Considering the bad efficiency and convergence of multi-objective evolutionary algorithms, this article introduces an elite-recombination-based hybrid multi-objective evolutionary algorithm (ERHMEA). In the algorithm, the multi-objective optimization problem was decomposed into multiple single-objective optimization problems and generated the only elite solution with the genetic-algorithm-based elite recombination strategy. Strategies such as regional population initialization, improved local search and selection mechanisms, optimized subgroup based packet crossover and adaptive multiple mutation operator, and chaos optimization based restart mechanism effectively overcome the inherent defects of elite preservation, as well as the multi-objective evolutionary algorithm (MEA) existing target space solution crowding, slow convergence, prematurity, and other issues. Multi-objective test functions analysis and experimental simulation prove the effectiveness and superiority of the proposed algorithm.