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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.<\/jats:p>","DOI":"10.1155\/2020\/6084917","type":"journal-article","created":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T19:20:06Z","timestamp":1604172006000},"page":"1-36","source":"Crossref","is-referenced-by-count":38,"title":["An Adaptive Chaotic Sine Cosine Algorithm for Constrained and Unconstrained Optimization"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5195-9375","authenticated-orcid":true,"given":"Yetao","family":"Ji","sequence":"first","affiliation":[{"name":"College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, China"}]},{"given":"Jiaze","family":"Tu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, China"}]},{"given":"Hanfeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, China"}]},{"given":"Wenyong","family":"Gui","sequence":"additional","affiliation":[{"name":"College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4754-6644","authenticated-orcid":true,"given":"Guoxi","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Wenzhou Polytechnic, Wenzhou 325035, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7714-9693","authenticated-orcid":true,"given":"Huiling","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, China"}]},{"given":"Mingjing","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam"}]}],"member":"311","reference":[{"key":"1","article-title":"Dynamic multi-swarm particle swarm optimizer with sub-regional harmony search","author":"S. 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