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An Overview of Multi-population Methods for Dynamic Environments

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Book cover Advances in Learning Automata and Intelligent Optimization

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 208))

Abstract

Dynamic optimization problems (DOPs) can be found almost everywhere, from ship navigation at sea (Michalewicz et al. 2007) to aerospace design (Mack et al. 2007). In general terms, all aspects of science and engineering include the optimization of a set of complex problems, in which the objectives of the optimization, some restrictions, or other elements may vary over time. Since exact algorithms are impractical in dynamic environments, stochastic optimization techniques have gained much popularity. Among them, evolutionary computation (EC) techniques have attracted a great deal of attention due to their potential for solving complex optimization problems. Nevertheless, EC methods should undergo certain adjustments to work well when applying on DOPs. Diversity loss is by far the most severe challenge to EC methods in DOPs. This issue appears due to the tendency of the individuals to converge to a single optimum. As a result, when the global optimum is shifted away, the number of function evaluations (FEs) required for a partially converged population to relocate the optimum is quite harmful to the performance. In this chapter, we provide an overview of the multi-population methods for dynamic environments.

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Javidan Kazemi Kordestani

  2. Department of Computer Engineering and Information Technology, Payame Noor University (PNU), P.O. BOX 19395-3697, Tehran, Iran

    Mehdi Razapoor Mirsaleh

  3. Department of Computer Engineering, University of Science and Culture, Tehran, Iran

    Alireza Rezvanian

  4. Computer Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Mohammad Reza Meybodi

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  1. Javidan Kazemi Kordestani
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  2. Mehdi Razapoor Mirsaleh
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  3. Alireza Rezvanian
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  4. Mohammad Reza Meybodi
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Correspondence to Alireza Rezvanian .

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Editors and Affiliations

  1. Department of Computer Engineering Science and Research Branch, Islamic Azad University, Tehran, Iran

    Javidan Kazemi Kordestani

  2. Department of Computer Engineering and Information Technology, Payame Noor University (PNU), Tehran, Iran

    Mehdi Razapoor Mirsaleh

  3. Department of Computer Engineering, University of Science and Culture, Tehran, Iran

    Alireza Rezvanian

  4. Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

    Mohammad Reza Meybodi

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Kazemi Kordestani, J., Razapoor Mirsaleh, M., Rezvanian, A., Meybodi, M.R. (2021). An Overview of Multi-population Methods for Dynamic Environments. In: Kazemi Kordestani, J., Mirsaleh, M.R., Rezvanian, A., Meybodi, M.R. (eds) Advances in Learning Automata and Intelligent Optimization. Intelligent Systems Reference Library, vol 208. Springer, Cham. https://doi.org/10.1007/978-3-030-76291-9_7

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