基于双种群两阶段变异策略的差分进化算法

Differential Evolution Algorithm Based on Two-stage Mutation Strategy of Two-population

针对差分进化算法差分策略优化问题上的不足,解决DE/best/1策略全局探测能力差, DE/rand/1局部搜索能力弱而带来的鲁棒性降低及陷入局部最优等问题,本文在差分策略上进行改进,并且加入邻域分治思想提高进化效率,提出一种基于双种群两阶段变异策略的差分进化算法(TPSDE).第一个阶段利用DE/best/1的优势对邻域向量划分完成的子种群区域进行局部优化,第二个阶段借鉴DE/rand/1的思想实现全局优化,最终两阶段向量加权得到最终变异个体使得算法避免了过早收敛和搜索停滞等问题的出现. 6个测试函数的仿真实验结果表明TPSDE在收敛速度、优化精度和鲁棒性方面都得到了明显改善.

The differential evolution algorithm is limited in the optimization of the differential strategy, the DE/best/1strategy has a poor global detection ability, and the weak local search ability of the DE/rand/1 strategy leads to the reduction in robustness and local optimal problems. In this study, the differential strategy is improved and the idea of neighborhood divide and conquer is added to improve the evolutionary efficiency. A differential evolution algorithm(TPSDE) based on two-stage mutation strategy with two populations is proposed. In the first stage, the advantages of the DE/best/1 strategy are employed to locally optimize the subpopulation area with completed neighborhood vector partition.In the second stage, the idea of the DE/rand/1 strategy is borrowed to achieve global optimization. Finally, the final variant individuals are obtained by weighting the vectors of the two stages, which avoids problems such as premature convergence and search stagnation. The simulation results of six test functions show that the TPSDE has significantly improved the convergence speed, optimization accuracy, and robustness.