Joint operations algorithm for large-scale global optimization

Gaoji Sun, Ruiqing Zhao, Yanfei Lan

Research output: Contribution to journalArticlepeer-review

33 Citations (SciVal)

Abstract

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.
Original languageEnglish
Pages (from-to)1025–1039
Number of pages15
JournalApplied Soft Computing
Volume38
Early online date30 Oct 2015
DOIs
Publication statusPublished - 31 Jan 2016
Externally publishedYes

Keywords

  • joint operations algorithm
  • meta-heuristic algorithms
  • evolutionary algorithms
  • swarm-based algorithms
  • large-scale global optimization

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