Differential Evolution with Secondary Mutation Strategies for Long-Term Search

For numerous years, researchers have extensively explored real parameter single-objective optimization by evolutionary computation. Among the various types of evolutionary algorithms, Differential Evolution (DE) performs outstandingly. Recently, the academic community has began concerning itself with long-term search. IMODE is a good DE algorithm for long-term search. The algorithm is based on two primary mutation strategies and one secondary. Within the population, the control ratio of each mutation strategy is determined by their respective performance outcomes. Sequential Quadratic Programming (SQP), an iterative method for continuous optimization, is employed on the best individual in the final stage of IMODE at a dynamic probability as a local search method. Based on the DE algorithm, we propose Differential Evolution with Secondary Mutation Strategies (SMSDE). In the proposed algorithm, more secondary mutation strategies are added, in addition to the original one used in IMODE. In each generation, just one of the secondary mutation strategies is activated based on history performance to cooperate with the two primary mutation strategies. In addition, at a dynamic probability, SQP is now called not only for the best individual in the final stage, but also for the worst individual among old ones in each generation. The experimental results demonstrate that SMSDE performs better than a number of state-of-the-art algorithms, including IMODE.