Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 21, Issue 4, Pages 644-660Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2017.2675628
Keywords
Computationally expensive problems; fitness estimation strategy (FES); particle swarm optimization (PSO); radial-basis-function networks; surrogate models
Funding
- National Natural Science Foundation of China [61403272, 61472269, 61525302, 61590922]
- Joint Research Fund for Overseas Chinese, Hong Kong and Macao Scholars of the National Natural Science Foundation of China [61428302]
- EPSRC [EP/M017869/1]
- State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, China
- Engineering and Physical Sciences Research Council [EP/M017869/1] Funding Source: researchfish
- EPSRC [EP/M017869/1] Funding Source: UKRI
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Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization (PSO) algorithm and a surrogate-assisted social learning-based PSO (SL-PSO) algorithm cooperatively search for the global optimum. The cooperation between the PSO and the SL-PSO consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the SL-PSO focuses on exploration while the PSO concentrates on local search. Empirical studies on six 50-D and six 100-D benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.
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