Hybrid Nature-Inspired Optimization Algorithm: Hydrozoan and Sea Turtle Foraging Algorithms for Solving Continuous Optimization Problems

In this paper, we develop a hybrid optimization algorithm inspired by the reproduction processes of hydrozoans and the foraging behavior of sea turtles for solving continuous optimization problems. Our hybrid algorithm combines the exploration capability of the hydrozoan algorithm with the exploitat...

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Veröffentlicht in:IEEE access Jg. 8; S. 65780 - 65800
Hauptverfasser: Tansui, Daranat, Thammano, Arit
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive algorithms
Approximation algorithms
Benchmark testing
Crossovers
Genetic algorithms
Hybrid algorithm
hydrozoan
nature-inspired algorithm
Operators (mathematics)
Optimization
Optimization algorithms
Organisms
Particle swarm optimization
sea turtle
Sea turtles
ISSN:2169-3536, 2169-3536
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Zusammenfassung:In this paper, we develop a hybrid optimization algorithm inspired by the reproduction processes of hydrozoans and the foraging behavior of sea turtles for solving continuous optimization problems. Our hybrid algorithm combines the exploration capability of the hydrozoan algorithm with the exploitation capability of the sea turtle foraging algorithm. Moreover, a new adaptive crossover operator was introduced and integrated into the hybrid algorithm to further enhance exploration capability. Our hybrid algorithm was evaluated and compared to the individual algorithms and 12 state-of-the-art algorithms. Results on 21 standard benchmark functions showed that our algorithm was very effective and was among the best of the group, specifically it converged faster than the individual algorithms on most functions and reached optimal or near-optimal results on all functions.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2984023