Adaptive memetic computing for evolutionary multiobjective optimization.

Inspired by biological evolution, a plethora of algorithms with evolutionary features have been proposed. These algorithms have strengths in certain aspects, thus yielding better optimization performance in a particular problem. However, in a wide range of problems, none of them are superior to one another. Synergetic combination of these algorithms is one of the potential ways to ameliorate their search ability. Based on this idea, this paper proposes an adaptive memetic computing as the synergy of a genetic algorithm, differential evolution, and estimation of distribution algorithm. The ratio of the number of fitter solutions produced by the algorithms in a generation defines their adaptability features in the next generation. Subsequently, a subset of solutions undergoes local search using the evolutionary gradient search algorithm. This memetic technique is then implemented in two prominent frameworks of multiobjective optimization: the domination- and decomposition-based frameworks. The performance of the adaptive memetic algorithms is validated in a wide range of test problems with different characteristics and difficulties.