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Learning Automata for Online Function Evaluation Management in Evolutionary Multi-population Methods for Dynamic Optimization Problems

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Advances in Learning Automata and Intelligent Optimization

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

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Abstract

Multi-population (MP) approach is among the most successful methods for solving continuous dynamic optimization problems (DOPs). Nevertheless, the MP approach has to conquer several obstacles to reach its maximum performance. One of these obstacles, which is the subject of this chapter, is how the MP methods exploit function evaluations (FEs). Since the calculation of FEs is the most expensive component of the evolutionary computation (EC) methods for solving real-world DOPs, we should find a way to spend a major portion of FEs around the most promising search area space. In generic form, the MP approach as a sub-population located far away from the optimal solution(s) is assigned the same amount of FEs as that near-optimal solution(s), which in turn exert deleterious effects on the performance of the optimization process. Therefore, one major challenge is how to suitably assign the FEs to each sub-population to enhance MP methods’ efficiency for DOPs. This chapter generalizes the application of variable-structure learning automaton (VSLA) and fixed-structure learning automaton (FSLA) for FE management to improve MP methods for DOPs. The present work is applied to DE-based MP methods, MP version of particle swarm optimization (PSO), firefly algorithm (FFA), and JAYA.

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Author information

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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Corresponding author

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). Learning Automata for Online Function Evaluation Management in Evolutionary Multi-population Methods for Dynamic Optimization Problems. 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_8

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