摘要
针对传统DE算法在求解复杂函数时会出现早熟收敛、收敛精度低、收敛速度慢等缺陷,提出了一种多策略自适应变异的差分进化算法MsA-DE。将3种变异策略两两结合,随机分配所占比重,以增加种群的多样性;通过引入进化程度阈值,自适应地选择最合适的变异策略,平衡算法的全局搜索和局部搜索能力;对越界的变异个体进行处理,保证种群的多样性和有效性。加入扰动机制提高算法跳出局部最优的能力,同时提高最优解的精度。将该算法用于14个测试函数的优化中,结果表明,MsA-DE算法与其它4种算法相比具有更高的收敛精度和跳出局部最优的能力。将该算法应用于铁路功率调节器RPC的容量优化问题中,结果表明,该算法能够减小RPC补偿装置的容量,提高装置的经济性。
In order to solve the defects of premature convergence,low convergence precision and slow convergence speed in the traditional DE algorithm,a multi-strategy adaptive mutation differential evolution algorithm(MsA-DE)is proposed.The algorithm combines three kinds of mutation strategies,randomly assigns the proportion,and increases the diversity of the population.By introducing the evolution threshold,the most appropriate mutation strategy is adaptively selected,and the global search and local search ability of the algorithm are balanced.Individuals crossing the boundary are treated to ensure the diversity and effectiveness of the population.Adding a perturbation mechanism improves the ability of the algorithm to jump out of local optimum,and at the same time improves the accuracy of obtaining the optimal solution.The algorithm is applied to the optimization of 14 test functions.The results show that the MsA-DE algorithm has higher convergence precision and the ability to jump out of local optimum,compared with the other three algorithms.The algorithm is applied to the capacity optimization problem of Railway Power Conditioner(RPC).The results show that the algorithm can reduce the capacity of the RPC compensation device and improve the economics of the device.
作者
胡福年
董倩男
HU Fu-nian;DONG Qian-nan(School of Electrical Engineering & Automation,Jiangsu Normal University,Xuzhou 221000,China)
出处
《计算机工程与科学》
CSCD
北大核心
2020年第6期1076-1088,共13页
Computer Engineering & Science
基金
徐州市科技计划项目(KC16SG253)
江苏师范大学研究生科研创新计划项目(2018YXJ077)。
关键词
差分进化算法
多变异策略
越界处理
自适应
容量优化
differential evolution algorithm
multi-variation strategy
out-of-bounds processing
adaptive
capacity optimization
