A multi-start central force optimization for global optimization |
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| Affiliation: | 1. Antai College of Economics & Management, Shanghai Jiao Tong University, Shanghai 200052, PR China;2. Department of Fundamental Sciences, Yancheng Institute of Technology, Yancheng, Jiangsu 224051, PR China;1. College of Foreign Studies, Yanshan University, No. 438 Hebei Street, Qinhuangdao 066004, Hebei, PR China;2. Institute of Electrical Engineering, Yanshan University, No. 438 Hebei Street, Qinhuangdao 066004, Hebei, PR China;3. College of International Programs, Shanghai International Studies University, No. 410 Dong Ti Yu Hui Road, Shanghai 200083, PR China;1. GREYC, UMR CNRS 6072 – ENSICAEN & Université de Caen, F-14050 Caen, France;2. Université Paris-Est, LIGM, UMR CNRS 8049, UPEM, F-77454 Marne-la-Vallée, France;3. Hôpital Saint-Joseph, F-75014 Paris, France;4. AP-HP, Hôpital H. Mondor, F-94000 Créteil, France;5. AP-HP, Hôpital Beaujon, F-92110 Clichy, France;6. Université Paris-Est, LISSI (EA 3956), UPEC, F-94010 Créteil, France |
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| Abstract: | Central force optimization (CFO) is an efficient and powerful population-based intelligence algorithm for optimization problems. CFO is deterministic in nature, unlike the most widely used metaheuristics. CFO, however, is not completely free from the problems of premature convergence. One way to overcome local optimality is to utilize the multi-start strategy. By combining the respective advantages of CFO and the multi-start strategy, a multi-start central force optimization (MCFO) algorithm is proposed in this paper. The performance of the MCFO approach is evaluated on a comprehensive set of benchmark functions. The experimental results demonstrate that MCFO not only saves the computational cost, but also performs better than some state-of-the-art CFO algorithms. MCFO is also compared with representative evolutionary algorithms. The results show that MCFO is highly competitive, achieving promising performance. |
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| Keywords: | Central force optimization Deterministic algorithm Multi-start strategy Global optimization |
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