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Electric fish optimization: a new heuristic algorithm inspired by electrolocation

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Abstract

Swarm behaviors in nature have inspired the emergence of many heuristic optimization algorithms. They have attracted much attention, particularly for complex problems, owing to their characteristics of high dimensionality, nondifferentiability, and the like. A new heuristic algorithm is proposed in this study inspired by the prey location and communication behaviors of electric fish. Nocturnal electric fish have very poor eyesight and live in muddy, murky water, where visual senses are very limited. Therefore, they rely on their species-specific ability called electrolocation to perceive their environment. The active and passive electrolocation capability of such fish is believed to be a good candidate for balancing local and global search, and hence it is modeled in this study. A new heuristic called electric fish optimization (EFO) is introduced and compared with six well-known heuristics (simulated annealing, SA; vortex search, VS; genetic algorithm, GA; differential evolution, DE; particle swarm optimization, PSO; and artificial bee colony, ABC). In the experiments, 50 basic and 30 complex mathematical functions, 13 clustering problems, and five real-world design problems are used as the benchmark sets. The simulation results indicate that EFO is better than or very competitive with its competitors.

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  1. https://wise.cs.hacettepe.edu.tr/projects/efo/.

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Acknowledgements

This work was supported by the National MSc and PhD Scholarship Programme for Senior Undergraduate Students (2228) of the Scientific and Technological Research Council of Turkey (or TUBITAK). The authors appreciate this support. In addition, the authors sincerely acknowledge and thank Hacettepe Teknokent Technology Transfer Center for advanced editing service to this article. Finally, the authors would also like to give special thanks to Dr. Bahriye AKAY for her valuable comments on our study.

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  1. WISE Lab, Hacettepe University, Ankara, Turkey

    Selim Yilmaz & Sevil Sen

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Appendices

Appendix 1

See Tables 17 and 18.

Table 17 Mathematical benchmark function set used in the first experiment
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Table 18 Complex mathematical benchmark set
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Appendix 2

See Figs. 10, 11, 12.

Fig. 10
figure 10

Comparative convergence characteristics of EFO for basic mathematical benchmark functions 1–25

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Fig. 11
figure 11

Comparative convergence characteristics of EFO for basic mathematical benchmark functions 26–50

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Fig. 12
figure 12

Comparative convergence characteristics of EFO for complex mathematical benchmark functions

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Yilmaz, S., Sen, S. Electric fish optimization: a new heuristic algorithm inspired by electrolocation. Neural Comput & Applic 32, 11543–11578 (2020). https://doi.org/10.1007/s00521-019-04641-8

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