An improved differential evolution with adaptive population allocation and mutation selection

Abstract

In order to improve the performance of differential evolution (DE) for different optimization problems, an improved DE with adaptive population allocation and mutation selection (iDE-APAMS) is proposed. First, different mutation strategies are selected to constitute the exploration strategy pool and the exploitation strategy pool. The exploration strategy pool is mainly used for global search to increase the population diversity; the exploitation strategy pool is primarily used for local search to accelerate the convergence and improve the solution accuracy. Second, different mutation strategies obtain population resources dynamically through cooperation and competition. Mutation strategies in the same strategy pool first compete for population resources with mutation strategies in the other strategy pool through cooperation, and in this way the population is allocated between the two strategy pools. Then within each strategy pool, different mutation strategies compete with each other to obtain corresponding population resources. Third, mutation scale factor and crossover rate are adaptively adjusted based on population diversity and fitness improvement. Finally, the Levy random walk is applied to individuals with better fitness at the later stage of the iteration to avoid falling into local optima and further improve the accuracy of the solution. The iDE-APAMS is compared with 4 classical DE variants and 11 state-of-the-art algorithms on the CEC2013, CEC2014, and CEC2017 benchmark test suites respectively, and it is also tested on 22 real-world optimization problems in CEC2011. The results show that iDE-APAMS can improve the convergence of the algorithm and the stability of the solution, and it is statistically significantly better than the comparison algorithms.