分区引导种群进化的拟态物理学多目标优化算法  

Artificial physics multi-objective optimization algorithm based on partition-guided population evolution

作　　者：孙宝[1] 张丽静 李占龙[2] 范凯[1] 靳琴琴 罗芸滢 Sun Bao;Zhang Lijing;Li Zhanlong;Fan Kai;Jin Qinqin;Luo Yunying(School of Applied Science,Taiyuan University of Science&Technology,Taiyuan 030024,China;School of Vehicle&Transportation Engineering,Taiyuan University of Science&Technology,Taiyuan 030024,China;Guizhou Jonyang Kinetics Co.,Ltd.,Guiyang 550025,China)

机构地区：[1]太原科技大学应用科学学院,太原030024 [2]太原科技大学车辆与交通工程学院,太原030024 [3]贵州詹阳动力重工有限公司,贵阳550025

出　　处：《计算机应用研究》2023年第5期1381-1388,1409,共9页Application Research of Computers

基　　金：国家自然科学基金资助项目(52272401,51805347);山西省基础研究计划资助项目(202203021211185);贵州省工业和信息化发展专项资金科技创新项目(KJ202102);山西省高等学校科技创新项目(2021L324)。

摘　　要：针对基本拟态物理学优化(artificial physics optimization, APO)算法易陷入局部最优、分布性不佳等问题,提出一种分区引导种群进化的改进多目标拟态物理学优化(multi-objective APO improved by partition-guided evolution, PEMOAPO)算法。首先,采用tent映射与反向学习相结合的策略进行种群的初始化,增强种群的多样性;其次,提出分区引导个体进行进化的机制,对处于可行域与不可行域的个体,采取不同的质量函数及虚拟作用力计算规则进行迭代更新,增强算法的收敛性能。选取MW系列和C_DTLZ系列作为基准测试函数进行仿真实验,通过综合性能评价指标对比分析、统计学分析、收敛性分析及时间复杂度分析,表明改进算法具有良好的多样性及收敛性,能快速收敛到Pareto前沿。To address the problems that the basic APO algorithm tends to fall into local optimum and poor distribution,this paper proposed a PEMOAPO algorithm.Firstly,this paper used the strategy of tent mapping and reverse learning to initialize the population and enhance the population diversity.Secondly,it introduced a mechanism of partition-guided individuals for evolution,and it adopted different mass functions and virtual force calculation rules to iteratively update the individuals in the feasible and infeasible domains to enhance the convergence performance of the algorithm.Choosing MW series and C_DTLZ series as the benchmark test functions in simulation experiments,and the comprehensive performance evaluation index compa-rison analysis,statistical analysis,convergence analysis and time complexity analysis show that the improved algorithm has good diversity and convergence,and can converge to the Pareto front quickly.

关 键 词：拟态物理学 多目标 非支配排序 分区进化 质量函数 虚拟作用力 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]