摘要
针对乌鸦搜索算法(CSA)优化高维问题时存在寻优精度低、局部极值逃逸能力弱等问题,提出一种耦合多个体变因子加权学习机制与最优个体邻代维度交叉策略的改进乌鸦搜索算法(ICSA).该算法随迭代进程动态修正模型控制参数(感知概率和飞行长度),利用多个体的变因子加权学习机制保证子代个体同时继承跟随乌鸦与上代最优个体的位置信息以避免单个体继承的过快种群同化并减小陷入局部极值的风险;同时构建历史最优个体的邻代维度交叉策略,并按维度绝对差异大的优先替换原则更新最优个体位置,以保留历代最优维度信息并提高算法的局部极值逃逸能力.数值实验结果分别验证了模型参数对CSA算法性能的一定影响,加权学习因子不同递变形式对ICSA算法性能改善的有效性与差异性以及改进算法的优越寻优性能.
Considering that crow search algorithm (CSA) has low optimization accuracy and weak local-optimum escape ability in optimizing high-dimensional problems,an improved crow search algorithm(ICSA) is proposed by coupling the variable-factors’ weighted learning mechanism of multiple individuals(Mi-VWL) and the adjacent-generations dimension crossover strategy of the best individual(Bi-ADC).In the proposed algorithm,the model parameters,i.e.awareness probability and flight length,are firstly modified dynamically with increasing number of iterations.Meanwhile,the Mi-VWL is introduced to guarantee that offspring individuals of crow population can inherit position information from the followed crow and the best individual of the last generation simultaneously,which is advantageous to avoid the over-rapid population intensification of single-individual learning and reduce the algorithm’s risk on dropping into local optimum.Furthermore,Bi-ADC is constructed and the priority replacement principle of larger absolute value difference of dimensions between two-generations is adopted to update position of the best individual,which is beneficial to retain the optimal dimension information of historical best crows and enhance the local extreme escape ability of algorithms.Experimental results verify the influence of modal parameters on CSA’s performance,the effectiveness and differences of different-type weighted learning factor on improving ICSA’s capability and the superior optimization ability of the proposed algorithm,respectively.
作者
赵世杰
高雷阜
于冬梅
徒君
ZHAO Shi-jie;GAO Lei-fu;YU Dong-mei;TU Jun(Institute of Optimization and Decision,Liaoning Technical University,Fuxin,Liaoning 123000,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2019年第1期40-48,共9页
Acta Electronica Sinica
基金
辽宁省博士启动基金(No.20170520075)
国家自然科学基金青年基金(No.51704140)
辽宁省教育厅基金(No.LJ2017QL031
No.LJYL043)
辽宁省社科规划基金(No.L17BGL004)
关键词
智能优化算法
乌鸦搜索算法
变因子加权学习机制
邻代维度交叉策略
基准测试函数
intelligent optimization algorithm
crow search algorithm
variable-factor weighted learning mechanism
adjacent-generations dimension crossover strategy
benchmark function
