Adaptive multimodal continuous ant colony optimization

Qiang Yang, Wei Neng Chen, Zhengtao Yu, Tianlong Gu, Yun Li, Huaxiang Zhang, Jun Zhang

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210 Citations (Scopus)
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Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization (ACO) algorithms in preserving high diversity, this paper intends to extend ACO algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ACO algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima.

Original languageEnglish
Article number7511696
Pages (from-to)191-205
Number of pages15
JournalIEEE Transactions on Evolutionary Computation
Issue number2
Early online date13 Jul 2016
Publication statusPublished - 1 Apr 2017


  • ant colony optimization (ACO)
  • multimodal optimization
  • multiple global optima
  • niching


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