Efficient Resource Allocation in Cooperative Co-Evolution for Large-Scale Global Optimization
Open Access
- 15 December 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Evolutionary Computation
- Vol. 21 (4), 493-505
- https://doi.org/10.1109/tevc.2016.2627581
Abstract
Cooperative co-evolution (CC) is an explicit means of problem decomposition in multipopulation evolutionary algorithms for solving large-scale optimization problems. For CC, subpopulations representing subcomponents of a large-scale optimization problem co-evolve, and are likely to have different contributions to the improvement of the best overall solution to the problem. Hence, it makes sense that more computational resources should be allocated to the subpopulations with greater contributions. In this paper, we study how to allocate computational resources in this context and subsequently propose a new CC framework named CCFR to efficiently allocate computational resources among the subpopulations according to their dynamic contributions to the improvement of the objective value of the best overall solution. Our experimental results suggest that CCFR can make efficient use of computational resources and is a highly competitive CCFR for solving large-scale optimization problems.Keywords
Funding Information
- National Natural Science Foundation of China (61305086, 61673355, 61673354, 61329302, 61305079)
- EPSRC (EP/K001523/1)
- Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing (KLIGIP201602)
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