基于自适应资源分配池的竞争合作群协同优化算法  

Competitive-Cooperative Coevolution for Large Scale Optimization with Computation Resource Allocation Pool

作　　者：潘燕娜 冯翔[1,2] 虞慧群 PAN Yan-na;FENG Xiang;YU Hui-qun(School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China;Shanghai Engineering Research Center of Smart Energy,Shanghai 200237,China)

机构地区：[1]华东理工大学信息科学与工程学院,上海200237 [2]上海智慧能源工程技术研究中心,上海200237

出　　处：《计算机科学》2022年第2期182-190,共9页Computer Science

基　　金：国家自然科学基金(61772200,61772201,61602175);上海市浦江人才计划(17PJ1401900);上海市经信委“信息化发展专项资金”(201602008)。

摘　　要：合作协同优化是目前针对大规模优化问题的最有前景的算法之一,该算法通过分而治之策略划分子问题,以进行协同进化。不同的子问题根据演化状态的不同对整体改善的贡献大小也不一致,因此均匀分配计算资源会造成浪费。针对上述问题,提出一种新颖的基于自适应资源分配池策略和基于竞争的群优化集成的竞争合作群协同优化算法。首先,考虑到子问题的不平衡性,将子问题对整体目标改善的动态贡献作为分配计算资源的标准;其次,为了更好地适应子问题演化状态,不固定资源分配单元,而是利用池模型进行自适应分配,并且在相同子问题连续迭代中避免重复评估个体,以节省计算资源;然后,将上述策略与基于竞争的群协同优化算法进行集成,设计了一种新的竞争合作群协同优化;最后,将该算法与其他5种算法在CEC 2010和CEC 2013套件的35个基准函数上进行比较,验证了算法的有效性。Through the strategy of divide and conquer,cooperative co-evolution(CC) has shown great prospects in evolutionary algorithm for solving large scale optimization problems.In CC,sub-problems have inconsistent contributions to the improvement of best overall solution according to different evolution states.Hence,evenly allocating computing resources will lead to waste.In response to the above-mentioned problem,a novel competitive-cooperative coevolution framework is proposed with adaptive resource allocation pool and competitive swarm optimization.Due to the imbalance of the sub-problems,the dynamic contribution of sub-problems is used as the criterion for allocating computing resources.For adapting to the evolution state of the sub-problems,pool model is exploited for adaptive allocation instead of fixed resource allocation unit.Specially,the framework is able to save computing resources by avoiding repeated evaluation of individuals in successive iterations of the same sub-problem.Then,competitive swarm optimization is combined with cooperative coevolution framework to improve efficiency.Compared with other five algorithms,experimental results on benchmark functions of the CEC 2010 and CEC 2013 suites for large scale optimization de-monstrate that the computation resource allocation pool is significant and the framework integrated with CSO shows highly competitive in solving large scale optimization problems.

关 键 词：合作协同 演化算法 大规模优化问题 计算资源分配 竞争群优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]