Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems

This paper proposes a new meta-heuristic algorithm inspired by horses’ herding behavior for high-dimensional optimization problems. This method, called the Horse herd Optimization Algorithm (HOA), imitates the social performances of horses at different ages using six important features: grazing, hie...

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Veröffentlicht in:Knowledge-based systems Jg. 213; S. 106711
Hauptverfasser: MiarNaeimi, Farid, Azizyan, Gholamreza, Rashki, Mohsen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 15.02.2021
Elsevier Science Ltd
Schlagworte:
Algorithms
Behavior
Behavior problems
Complexity
Defense mechanisms
Global optimization
Grazing
Heuristic
Heuristic methods
High dimension
Horses
Horse’s life
Imitation
Meta-heuristic
Optimization
Optimization algorithms
Pastoralism
Sensitivity analysis
Sociability
Swarm intelligence
Trigonometric functions
Wolves
High dimension
Global optimization
Horse’s life
Swarm intelligence
Meta-heuristic
ISSN:0950-7051, 1872-7409
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Zusammenfassung:This paper proposes a new meta-heuristic algorithm inspired by horses’ herding behavior for high-dimensional optimization problems. This method, called the Horse herd Optimization Algorithm (HOA), imitates the social performances of horses at different ages using six important features: grazing, hierarchy, sociability, imitation, defense mechanism and roam. The HOA algorithm is created based on these behaviors, which has not existed in the history of studies so far. A sensitivity analysis is also performed to obtain the best values of coefficients used in the algorithm. HOA has a very good performance in solving complex problems in high dimensions, due to the large number of control parameters based on the behavior of horses at different ages. The proposed algorithm is compared with popular nature-inspired optimization algorithms, including grasshopper optimization algorithm (GOA), sine cosine algorithm (SCA), multi-verse optimizer (MVO), moth–flame optimizer (MFO), dragonfly algorithm (DA), and grey​ wolf optimizer (GWO). Solving several high-dimensional benchmark functions (up to 10,000 dimensions) shows that the proposed algorithm is highly efficient for high-dimensional global optimization problems. The HOA algorithm also outperforms the mentioned popular optimization algorithms for the case of accuracy and efficiency with lowest computational cost and complexity.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2020.106711