A hybrid adaptive evolutionary algorithm in the domination-based and decomposition-based frameworks of multi-objective optimization

Under the framework of evolutionary paradigms, many variations of evolutionary algorithms have been designed. Each of the algorithms performs well in certain cases and none of them are dominating one another. This study is based on the idea of synthesizing different evolutionary algorithms so as to complement the limitations of each algorithm. On top of this idea, this paper proposes an adaptive mechanism that synthesizes a genetic algorithm, differential evolution and estimation of distribution algorithm. The adaptive mechanism takes into account the ratio of the number of promising solutions generated from each optimizer in an early stage of evolutions so as to determine the proportion of the number of solutions to be produced by each optimizer in the next generation. Furthermore, the adaptive algorithm is also hybridized with the evolutionary gradient search to further enhance its search ability. The proposed hybrid adaptive algorithm is developed in the domination-based and decomposition-based multi-objective frameworks. An extensive experimental study is carried out to test the performances of the proposed algorithms in 38 state-of-the-art benchmark test instances.