基于多样性全局最优引导和反向学习的离子运动算法  

Ions motion optimization algorithm based on diversity optimal guidance and opposition-based learning

作　　者：汪超[1,2] 王丙柱 岑豫皖 谢能刚 WANG Chao;WANG Bing-zhu;CEN Yu-wan;XIE Neng-gang(College of Mechanical Engineering,Anhui University of Technology,Maánshan243002,China;College of Mechanics and Materials,Hehai University,Nanjing210098,China;Engineering Research Center,Hydraulic Vibration and Control of Ministry of Education,Maánshan243002,China;National Machine Quality Supervision and Inspection Center,Maánshan243002,China)

机构地区：[1]安徽工业大学机械工程学院,安徽马鞍山243002 [2]河海大学力学与材料学院,南京210098 [3]液压振动与控制教育部工程研究中心,安徽马鞍山243002 [4]国家机床产品质量监督检验中心,安徽马鞍山243002

出　　处：《控制与决策》2020年第7期1584-1596,共13页Control and Decision

基　　金：国家自然科学基金项目(61375068);安徽省科技攻关面上项目(1704a0902008);安徽工业大学校青年基金项目(RD18100232)。

摘　　要：针对离子运动算法空间探索能力和开发能力的不足,提出一种改进算法.在离子运动算法的液态阶段中,该算法嵌入一种多样性反馈搜索机制和全局最优引导策略的算法结构;同时,优化算法晶态阶段中的初始化过程采用反向学习方法生成,其中,初始化概率采用动态惯性改变方式.经过国际上通用的23个基准函数测试,与一些流行的元启发式算法比较,并从平均收敛值、方差、Wilcoxon符号秩检验、收敛成功率以及最优收敛时间等方面进行综合评估,从而表明所提出算法的有效性.To the weaknesse of space exploration and exploitation of ions motion algorithms,an improved algorithm is proposed.In the liquid phase of the ions motion algorithm,a diversity feedback search mechanism and a global optimal guidance strategy are embedded in the algorithm structure.Meanwhile,the initialization process in the crystal phase of the optimization algorithm is generated by using the opposition-based learning method,in which the initialization probability is changed by dynamic inertia.The proposed algorithm is tested using the 23 international bench mark functions and compared with some popular meta-heuristics algorithms,furthermore are comprehensively evaluated in terms of the average convergence value,variance,Wilcoxon’s sign rank test,convergence success rate and optimal convergence time.The results show the effectiveness of the proposed algorithm.

关 键 词：离子运动算法 全局最优引导 多样性反馈 反向学习 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]