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An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained Optimization

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  • Yetao Ji
  • Jiaze Tu
  • Hanfeng Zhou
  • Wenyong Gui
  • Guoxi Liang
  • Huiling Chen
  • Mingjing Wang

Abstract

Sine cosine algorithm (SCA) is a new meta-heuristic approach suggested in recent years, which repeats some random steps by choosing the sine or cosine functions to find the global optimum. SCA has shown strong patterns of randomness in its searching styles. At the later stage of the algorithm, the drop of diversity of the population leads to locally oriented optimization and lazy convergence when dealing with complex problems. Therefore, this paper proposes an enriched SCA (ASCA) based on the adaptive parameters and chaotic exploitative strategy to alleviate these shortcomings. Two mechanisms are introduced into the original SCA. First, an adaptive transformation parameter is proposed to make transformation more flexible between global search and local exploitation. Then, the chaotic local search is added to augment the local searching patterns of the algorithm. The effectiveness of the ASCA is validated on a set of benchmark functions, including unimodal, multimodal, and composition functions by comparing it with several well-known and advanced meta-heuristics. Simulation results have demonstrated the significant superiority of the ASCA over other peers. Moreover, three engineering design cases are employed to study the advantage of ASCA when solving constrained optimization tasks. The experimental results have shown that the improvement of ASCA is beneficial and performs better than other methods in solving these types of problems.

Suggested Citation

  • Yetao Ji & Jiaze Tu & Hanfeng Zhou & Wenyong Gui & Guoxi Liang & Huiling Chen & Mingjing Wang, 2020. "An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained Optimization," Complexity, Hindawi, vol. 2020, pages 1-36, October.
  • Handle: RePEc:hin:complx:6084917
    DOI: 10.1155/2020/6084917
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    Cited by:

    1. Amin Iraji & Javad Karimi & Suraparb Keawsawasvong & Moncef L. Nehdi, 2022. "Minimum Safety Factor Evaluation of Slopes Using Hybrid Chaotic Sand Cat and Pattern Search Approach," Sustainability, MDPI, vol. 14(13), pages 1-23, July.
    2. Chen, Chengcheng & Wang, Xianchang & Yu, Helong & Wang, Mingjing & Chen, Huiling, 2021. "Dealing with multi-modality using synthesis of Moth-flame optimizer with sine cosine mechanisms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 291-318.
    3. Liu, Yun & Heidari, Ali Asghar & Ye, Xiaojia & Liang, Guoxi & Chen, Huiling & He, Caitou, 2021. "Boosting slime mould algorithm for parameter identification of photovoltaic models," Energy, Elsevier, vol. 234(C).

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