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A survey on evolutionary computation for complex continuous optimization

The Artificial intelligence review, 2022-01, Vol.55 (1), p.59-110 [Peer Reviewed Journal]

The Author(s) 2021 ;COPYRIGHT 2022 Springer ;The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-021-10042-y

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  • Title:
    A survey on evolutionary computation for complex continuous optimization
  • Author: Zhan, Zhi-Hui ; Shi, Lin ; Tan, Kay Chen ; Zhang, Jun
  • Subjects: Algorithms ; Artificial Intelligence ; Cloud computing ; Computer Science ; Constraints ; Evolutionary algorithms ; Evolutionary computation ; Global optimization ; Internet of Things ; Multiple objective analysis ; Optimization ; Taxonomy
  • Is Part Of: The Artificial intelligence review, 2022-01, Vol.55 (1), p.59-110
  • Description: Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including M any-dimensions, M any-changes, M any-optima, M any-constraints, and M 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 reducing problem difficulty , increasing algorithm diversity , accelerating convergence speed , reducing running time , and extending application field . 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.
  • Publisher: Dordrecht: Springer Netherlands
  • Language: English
  • Identifier: ISSN: 0269-2821
    EISSN: 1573-7462
    DOI: 10.1007/s10462-021-10042-y
  • Source: ProQuest One Psychology
    Springer Nature OA/Free Journals
    ProQuest Central
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