Electric fish optimization: a new heuristic algorithm inspired by electrolocation

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...

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Bibliographic Details
Published in:Neural computing & applications 2020-08, Vol.32 (15), p.11543-11578
Main Authors: Yilmaz, Selim, Sen, Sevil
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Clustering
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer simulation
Data Mining and Knowledge Discovery
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Heuristic
Heuristic methods
Image Processing and Computer Vision
Mathematical functions
Optimization
Original Article
Particle swarm optimization
Probability and Statistics in Computer Science
Simulated annealing
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Summary: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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04641-8