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Joint operations algorithm for large-scale global optimization
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DOI：10.1016/j.asoc.2015.10.047.png)
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摘要
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Large-scale global optimization (LSGO) is a very important but thorny task in optimization domain, which widely exists in management and engineering problems. In order to strengthen the effectiveness of meta-heuristic algorithms when handling LSGO problems, we propose a novel meta-heuristic algorithm, which is inspired by the joint operations strategy of multiple military units and called joint operations algorithm (JOA). The overall framework of the proposed algorithm involves three main operations: offensive, defensive and regroup operations. In JOA, offensive operations and defensive operations are used to balance the exploration ability and exploitation ability, and regroup operations is applied to alleviate the problem of premature convergence. To evaluate the performance of the proposed algorithm, we compare JOA with six excellent meta-heuristic algorithms on twenty LSGO benchmark functions of IEEE CEC 2010 special session and four real-life problems. The experimental results show that JOA performs steadily, and it has the best overall performance among the seven compared algorithms. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Joint operations algorithm
Meta-heuristic algorithms
Evolutionary algorithms
Swarm based algorithms
Large-scale global optimization
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IF:
6.6
论文数:
1.4W
被引数:
4.8W
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