A Switchable Multi-Objective Grey Wolf Optimization Algorithm Based on Decomposition

In this paper, a switchable multi-objective grey wolf optimization algorithm based on decomposition (SMOGW/D) is proposed to deal with muti-objective problems. Specifically, an improved solution update method incorporated the original crossover/mutation operations and the Levy flight strategy, which benefits the global search ability of the algorithm. After that, a mechanism for judging the evolutionary state of population based on the number of neighborhood updates is proposed. According to the population evolutionary state, the update strategy of individual is switched and the neighborhood size of each subproblem can be adaptively adjusted. At last, proposed SMOGW/D is evaluated comprehensively on a series of benchmark functions. Experimental results have demonstrated the superiority of SMOGW/D against other five advanced methods on each indicator in most cases, which fully validates the effectiveness of enhanced strategies.