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Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including\n M<\/jats:italic>\n any-dimensions,\n M<\/jats:italic>\n any-changes,\n M<\/jats:italic>\n any-optima,\n M<\/jats:italic>\n any-constraints, and\n M<\/jats:italic>\n any-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including\n reducing problem difficulty<\/jats:italic>\n ,\n increasing algorithm diversity<\/jats:italic>\n ,\n accelerating convergence speed<\/jats:italic>\n ,\n reducing running time<\/jats:italic>\n , and\n extending application field<\/jats:italic>\n . Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.\n <\/jats:p>","DOI":"10.1007\/s10462-021-10042-y","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T11:04:28Z","timestamp":1627383868000},"page":"59-110","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":373,"title":["A survey on evolutionary computation for complex continuous optimization"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0862-0514","authenticated-orcid":false,"given":"Zhi-Hui","family":"Zhan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kay Chen","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,7,27]]},"reference":[{"key":"10042_CR1","doi-asserted-by":"crossref","first-page":"2667","DOI":"10.1007\/s00521-020-05163-4","volume":"33","author":"SA Abdulkarim","year":"2021","unstructured":"Abdulkarim SA, Engelbrecht AP (2021) Time series forecasting with feedforward neural networks trained using particle swarm optimizers for dynamic environments. 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