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A Survey of Metaheuristic Algorithms for Solving Optimization Problems

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Metaheuristics in Machine Learning: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 967))

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Abstract

With boosting of information technology, metaheuristics have gained considerable popularity, besides a huge number of optimization problems arises in several fields, therefore the need to concern with developing of metaheuristics algorithms and applying these algorithms for solving optimization problems within different scientific fields, become one of important areas to strength the active role of metaheuristic algorithms in solving real life optimization problems. In this context, this chapter introduces a review of applying the single objective and multi-objective optimization algorithms on different optimization problems types for solving engineering design problems, enhancing feature selection process, and other various fields, overall challenges and future trends are discussed.

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Authors and Affiliations

  1. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein & Waleed M. Mohamed

  2. Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt

    Mohamed A. Mahdy & Doaa Shebl

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  1. Essam H. Houssein
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  2. Mohamed A. Mahdy
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  3. Doaa Shebl
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  4. Waleed M. Mohamed
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Correspondence to Essam H. Houssein .

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Editors and Affiliations

  1. Computer Sciences Department, CUCEI, University of Guadalajara, Guadajalara, Jalisco, Mexico

    Diego Oliva

  2. Department of Computer Science, Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein

  3. Computer Sciences Department, CUCEI, University of Guadalajara, Guadajalara, Jalisco, Mexico

    Salvador Hinojosa

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Houssein, E.H., Mahdy, M.A., Shebl, D., Mohamed, W.M. (2021). A Survey of Metaheuristic Algorithms for Solving Optimization Problems. In: Oliva, D., Houssein, E.H., Hinojosa, S. (eds) Metaheuristics in Machine Learning: Theory and Applications. Studies in Computational Intelligence, vol 967. Springer, Cham. https://doi.org/10.1007/978-3-030-70542-8_21

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