Multiple Mutation Strategies Differential Evolution With the Best Individuals Allocated to the Best Performer Among the Strategies

摘要: Real parameter single objective optimization has been a prominent field for these decades. Recently, long-term search of real parameter single objective optimization is widely concerned based on the fact that solving difficulty always scales exponentially with the increase of dimensionality of solution space. So far, a number of population-based metaheuristics have been proposed. Among the algorithms, IMODE - a differential evolution algorithm based on three mutation strategies and the binomial or exponential crossover - demonstrates good performance. In this paper, based on IMODE, we propose multiple mutation strategies Differential Evolution with the Best Individuals allocated to the Best performer among the Strategies - BIBSDE - by revising IMODE. Altogether, we make five revisions in algorithm behavior and a change in parameter setting. The most important revision is that, during execution, for the next generation, the current best individuals are allocated to the best performer among the three mutation strategies as reward. Experimental results show that our BIBSDE performs better or at least not worse than existing population based metaheuristics for long-term search. Besides, each measure proposed by us is effective for enhancement.