Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (2): 182-190.doi: 10.11896/jsjkx.201200012

• Database & Big Data & Data Science • Previous Articles     Next Articles

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

PAN Yan-na, FENG Xiang, YU Hui-qun   

  1. School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,ChinaShanghai Engineering Research Center of Smart Energy,Shanghai 200237,China
  • Received:2020-12-01 Revised:2021-04-21 Online:2022-02-15 Published:2022-02-23
  • About author:PAN Yan-na,born in 1996,postgra-duate.Her main research interests include evolutionary computation and swarm intelligence.
    FENG Xiang,born in 1977,Ph.D,asso-ciate professor,is a member of China Computer Federation.Her main research interests include distributed swarm intelligence and evolutionary computing,search algorithms based on learning architecture and space-time big data intelligence.
  • Supported by:
    National Natural Science Foundation of China(61772200,61772201,61602175),Shanghai Pujiang Talent Program(17PJ1401900) and Shanghai Economic and Information Commission “Special Fund for Information Development”(201602008).

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Abstract: 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.

Key words: Competitive swarm optimization, Computation resource allocation, Cooperative coevolution, Evolutionary computation, Large scale optimization problems

CLC Number: 

  • TP183
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