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...
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| Autor*in: |
PAN Yan-na, FENG Xiang, YU Hui-qun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Chinesisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Jisuanji kexue - Editorial office of Computer Science, 2021, 49(2022), 2, Seite 182-190 |
|---|---|
| Übergeordnetes Werk: |
volume:49 ; year:2022 ; number:2 ; pages:182-190 |
| Links: |
|---|
| DOI / URN: |
10.11896/jsjkx.201200012 |
|---|
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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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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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https://doi.org/10.11896/jsjkx.201200012 https://doaj.org/article/dcd6fdfbcd274382a4441cca7327ca51 https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-2-182.pdf https://doaj.org/toc/1002-137X |
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10.11896/jsjkx.201200012 |
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2024-07-03T21:58:30.216Z |
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7.40026 |