期刊
ENGINEERING OPTIMIZATION
卷 46, 期 11, 页码 1465-1484出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/0305215X.2013.854349
关键词
evolutionary algorithm; elites; biogeography-based optimization; particle swarm optimization; fuzzy strategy
资金
- National Natural Science Foundation of China [70871091, 61075064, 61034004, 61005090]
- Programme for New Century Excellent Talents in University of Ministry of Education of China
- PhD Programmes Foundation of Ministry of Education of China [20100072110038]
- scientific research project of science and technology bureau of Jiaxing [2011BY7003]
- Singapore Academic Research Fund [R397000139133, R397000157112]
- NUS Teaching Enhancement [C397000039001]
In evolutionary algorithms, elites are crucial to maintain good features in solutions. However, too many elites can make the evolutionary process stagnate and cannot enhance the performance. This article employs particle swarm optimization (PSO) and biogeography-based optimization (BBO) to propose a hybrid algorithm termed biogeography-based particle swarm optimization (BPSO) which could make a large number of elites effective in searching optima. In this algorithm, the whole population is split into several subgroups; BBO is employed to search within each subgroup and PSO for the global search. Since not all the population is used in PSO, this structure overcomes the premature convergence in the original PSO. Time complexity analysis shows that the novel algorithm does not increase the time consumption. Fourteen numerical benchmarks and four engineering problems with constraints are used to test the BPSO. To better deal with constraints, a fuzzy strategy for the number of elites is investigated. The simulation results validate the feasibility and effectiveness of the proposed algorithm.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据