基于多指标精英个体博弈机制的多目标优化算法  

Multi-objective Optimization Algorithm Based on Multi-index Elite Individual Game Mechanism

作　　者：王旭 季伟东[1] 周国辉[1] 杨佳慧 Wang Xu;Ji Weidong;Zhou Guohui;Yang Jiahui(College of Computer Science and Information Engineering,Harbin Normal University,Harbin 150025,China)

机构地区：[1]哈尔滨师范大学计算机科学与信息工程学院,黑龙江哈尔滨150025

出　　处：《系统仿真学报》2023年第3期494-514,共21页Journal of System Simulation

基　　金：国家自然科学基金(31971015);黑龙江省自然科学基金(LH2021F037);哈尔滨市科技局科技创新人才研究专项(2017RAQXJ050);哈尔滨师范大学硕士研究生创新项目(HSDSSCX2021-119)。

摘　　要：为了改善多目标优化算法的收敛性与优化解集的多样性,缓解种群在目标空间中的坍缩,提出一种基于多指标精英个体博弈机制的多目标优化算法。利用Pareto支配关系与多指标综合筛选精英个体。将带有K-means聚类的精英个体博弈机制与交叉变异策略融合,有效提升了算法的收敛性与多样性。对算法进行了详细的收敛性分析,证明了算法的收敛性。将8个代表性的比较算法在标准测试函数上进行解集指标对比并解决实际水泵调度问题,本文算法在收敛性与多样性上优于或持平其他比较算法,验证了本文算法的有效性,在一定程度上减小了种群在目标空间中坍缩的概率。In order to improve the convergence of multi-objective optimization algorithm and the diversity of optimization solution set,and alleviate the flown down of population in target space,a multiobjective optimization algorithm based on multi-attribute elite individual game mechanism is proposed.This paper uses Pareto dominance relationship and multi-index to comprehensively screen elite individuals.The elite individual game mechanism with K-means clustering is integrated with cross and mutation strategy,which effectively improves the convergence and diversity of the algorithm.A detailed convergence analysis of the algorithm is performed to prove the convergence of the algorithm.Eight representative comparison algorithms are compared on the standard test function to solve the actual pump scheduling problem.The convergence and diversity of the algorithm in this paper are better than or equal to other comparison algorithms,verifying the effectiveness of the algorithm and reducing the probability of population flown down in the target space to a certain extent.

关 键 词：多目标优化 收敛性分析 博弈机制 K-MEANS聚类 精英个体筛选 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] TP391.9[自动化与计算机技术—计算机科学与技术]