计算机科学
水准点(测量)
数学优化
元启发式
集合(抽象数据类型)
趋同(经济学)
最优化问题
进化算法
人口
全局优化
优化算法
连续优化
算法
多群优化
人工智能
数学
人口学
地理
程序设计语言
经济
社会学
经济增长
大地测量学
标识
DOI:10.1016/j.asoc.2014.10.031
摘要
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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