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Emperor Penguins Colony: a new metaheuristic algorithm for optimization

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Abstract

A metaheuristic is a high-level problem independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. Metaheuristic algorithms attempt to find the best solution out of all possible solutions of an optimization problem. A very active area of research is the design of nature-inspired metaheuristics. Nature acts as a source of concepts, mechanisms and principles for designing of artificial computing systems to deal with complex computational problems. In this paper, a new metaheuristic algorithm, inspired by the behavior of emperor penguins which is called Emperor Penguins Colony (EPC), is proposed. This algorithm is controlled by the body heat radiation of the penguins and their spiral-like movement in their colony. The proposed algorithm is compared with eight developed metaheuristic algorithms. Ten benchmark test functions are applied to all algorithms. The results of the experiments to find the optimal result, show that the proposed algorithm is better than other metaheuristic algorithms.

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Fig. 1

Available in: https://www.bas.ac.uk/

Fig. 2

Available in Gilbert et al. [37]

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Original image is in landscapes & cycles: An environmentalist’s journey to climate skepticism by Jim Steele

Fig. 4

Image and graph created by Gerum and Zitterbart and available in paper with title “The origin of traveling waves in an emperor penguin huddle”, published by the open access new journal of physics [35]

Fig. 5

Original image taken by Stephanie Jenouvrier, Woods Hole Oceanographic Institution

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Original image taken by Fred https://www.Olivier/naturepl.com

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Authors and Affiliations

  1. Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

    Sasan Harifi, Madjid Khalilian & Javad Mohammadzadeh

  2. Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

    Sadoullah Ebrahimnejad

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  1. Sasan Harifi
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  2. Madjid Khalilian
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  3. Javad Mohammadzadeh
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  4. Sadoullah Ebrahimnejad
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Correspondence to Madjid Khalilian.

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Appendix A

Appendix A

The results of applying the PSO and DE algorithms on test functions for 100, 500 and 1000 dimensions.

See Table 10.

Table 10 Mean of best function values obtained for 100 iterations by PSO and DE with high dimensions
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Harifi, S., Khalilian, M., Mohammadzadeh, J. et al. Emperor Penguins Colony: a new metaheuristic algorithm for optimization. Evol. Intel. 12, 211–226 (2019). https://doi.org/10.1007/s12065-019-00212-x

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