Competitive-Cooperative Coevolution for Large Scale Optimization with Computation Resource Allocation Pool
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 s...
Ausführliche Beschreibung
Autor*in: |
PAN Yan-na, FENG Xiang, YU Hui-qun [verfasserIn] |
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E-Artikel |
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Sprache: |
Chinesisch |
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2022 |
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Übergeordnetes Werk: |
In: Jisuanji kexue - Editorial office of Computer Science, 2021, 49(2022), 2, Seite 182-190 |
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Übergeordnetes Werk: |
volume:49 ; year:2022 ; number:2 ; pages:182-190 |
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DOI / URN: |
10.11896/jsjkx.201200012 |
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Katalog-ID: |
DOAJ039166155 |
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520 | |a 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. | ||
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10.11896/jsjkx.201200012 doi (DE-627)DOAJ039166155 (DE-599)DOAJdcd6fdfbcd274382a4441cca7327ca51 DE-627 ger DE-627 rakwb chi QA76.75-76.765 T1-995 PAN Yan-na, FENG Xiang, YU Hui-qun verfasserin aut Competitive-Cooperative Coevolution for Large Scale Optimization with Computation Resource Allocation Pool 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. cooperative coevolution|evolutionary computation|large scale optimization problems|computation resource allocation|competitive swarm optimization Computer software Technology (General) In Jisuanji kexue Editorial office of Computer Science, 2021 49(2022), 2, Seite 182-190 (DE-627)DOAJ078619254 1002137X nnns volume:49 year:2022 number:2 pages:182-190 https://doi.org/10.11896/jsjkx.201200012 kostenfrei https://doaj.org/article/dcd6fdfbcd274382a4441cca7327ca51 kostenfrei https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-2-182.pdf kostenfrei https://doaj.org/toc/1002-137X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 49 2022 2 182-190 |
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10.11896/jsjkx.201200012 doi (DE-627)DOAJ039166155 (DE-599)DOAJdcd6fdfbcd274382a4441cca7327ca51 DE-627 ger DE-627 rakwb chi QA76.75-76.765 T1-995 PAN Yan-na, FENG Xiang, YU Hui-qun verfasserin aut Competitive-Cooperative Coevolution for Large Scale Optimization with Computation Resource Allocation Pool 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. cooperative coevolution|evolutionary computation|large scale optimization problems|computation resource allocation|competitive swarm optimization Computer software Technology (General) In Jisuanji kexue Editorial office of Computer Science, 2021 49(2022), 2, Seite 182-190 (DE-627)DOAJ078619254 1002137X nnns volume:49 year:2022 number:2 pages:182-190 https://doi.org/10.11896/jsjkx.201200012 kostenfrei https://doaj.org/article/dcd6fdfbcd274382a4441cca7327ca51 kostenfrei https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-2-182.pdf kostenfrei https://doaj.org/toc/1002-137X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 49 2022 2 182-190 |
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Competitive-Cooperative Coevolution for Large Scale Optimization with Computation Resource Allocation Pool |
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. |
abstractGer |
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. |
abstract_unstemmed |
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. |
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Competitive-Cooperative Coevolution for Large Scale Optimization with Computation Resource Allocation Pool |
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2024-07-03T21:58:30.216Z |
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