Journal
KNOWLEDGE-BASED SYSTEMS
Volume 109, Issue -, Pages 174-186Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2016.07.001
Keywords
Cooperative co-evolutionary algorithm; Dynamic landscapes; Multi-population mechanism
Categories
Funding
- National Nature Science Foundation of China [61105068, 61473233]
- Fundamental Research Funds for the Central Universities of China [3102016ZY007]
- EPSRC grant [EP/M017869/1]
- Joint Research Fund for Overseas Chinese, Hong Kong and Macao Scholars of the National Natural Science Foundation of China [61428302]
- EPSRC [EP/M017869/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/M017869/1] Funding Source: researchfish
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Cooperative co-evolutionary algorithm (CCEA) decomposes a problem into several subcomponents and optimizes them separately. This divide-and-conquer feature endows CCEAs with the capability of distributed and high-efficiency problem solving. However, traditional CCEAs trend to converge to Nash equilibrium rather than the global optimum due to information loss accompanied with problem decomposition. Moreover, the interactive nature makes the subcomponents' landscapes dynamic, which increases the challenge to conduct global optimization. To address these problems, a multi-population mechanism based CCEA (mCCEA) was proposed to compensate information in dynamic landscapes. The mCCEA is decentralized for each subcomponent since it doesn't need centralized archive or information sharing. It focuses on both the global and the local optima of each subcomponent by maintaining multiple populations and conducting local search in dynamic landscapes. These optima are seen as the current representatives of the subcomponents and used by the other subcomponents to construct their complete solutions for fitness evaluation. Experimental study was conducted based on a wide range of benchmark functions. The performance of the proposed algorithm was compared with several peer algorithms from the literature. The experimental results show effectiveness and advantage of the proposed algorithm. (C) 2016 Elsevier B.V. All rights reserved.
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