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Nevertheless, those variants of DE do not obtain the promising performance in solving black box problems with unknown features, which is mainly because the adaptive rules of those variants are designed according to their designers\u2019 cognition on the problem features. To enhance the optimization ability of DE in optimizing black box problems with unknown features, a differential evolution with autonomous selection of mutation strategies and control parameters (ASDE) is proposed in this paper, inspired by autonomous decision\u2010making mechanism of reinforcement learning. In ASDE, a historical experience archive with population features is utilized to preserve accumulated historical experience of the combination of mutation strategies and control parameters. Furthermore, the accumulated historical experience can be autonomously mapped into rules repository, and the individuals can choose the combination of mutation strategies and control parameters according to those rules. Additionally, an updating and utilization mechanism of the historical experience is designed to assure that the historical experience can be effectively accumulated and utilized efficiently. Compared with some state\u2010of\u2010the\u2010art intelligence algorithms on 15 functions of CEC2015, 28 functions of CEC2017, and parameter extraction problems of the photovoltaic model, ASDE has the advantages of solution accuracy, convergence speed, and robustness in solving black box problems with unknown features.<\/jats:p>","DOI":"10.1155\/2022\/7275088","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T19:50:17Z","timestamp":1658433017000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Differential Evolution with Autonomous Selection of Mutation Strategies and Control Parameters and Its Application"],"prefix":"10.1155","volume":"2022","author":[{"given":"Zhenyu","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7543-1927","authenticated-orcid":false,"given":"Zijian","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Zhiqiang","family":"Du","sequence":"additional","affiliation":[]},{"given":"Haowen","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Binhui","family":"Han","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Tian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9439-6645","authenticated-orcid":false,"given":"Fuxi","family":"Liu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2022,7,21]]},"reference":[{"key":"e_1_2_11_1_2","article-title":"Differential Evolution-A simple and efficient adaptive scheme for global optimization over continuous spaces","volume":"23","author":"Storn R. 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