A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search

A novel optimization algorithm is presented, inspired by group hunting of animals such as lions, wolves, and dolphins. Although these hunters have differences in the way of hunting, they are common in that all of them look for a prey in a group. The hunters encircle the prey and gradually tighten th...

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Bibliographic Details
Published in:Computers & mathematics with applications (1987) Vol. 60; no. 7; pp. 2087 - 2098
Main Authors: Oftadeh, R., Mahjoob, M.J., Shariatpanahi, M.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.10.2010
Subjects:
Algorithms
Animals
Benchmarking
Continuous optimization problems
Group hunting
Hunting
Mathematical models
Meta-heuristic algorithm
Optimization
Robustness
Searching
Wolves
Group hunting
Meta-heuristic algorithm
Continuous optimization problems
ISSN:0898-1221, 1873-7668
Online Access:Get full text
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Summary:A novel optimization algorithm is presented, inspired by group hunting of animals such as lions, wolves, and dolphins. Although these hunters have differences in the way of hunting, they are common in that all of them look for a prey in a group. The hunters encircle the prey and gradually tighten the ring of siege until they catch the prey. In addition, each member of the group corrects its position based on its own position and the position of other members. If the prey escapes from the ring, hunters reorganize the group to siege the prey again. Several benchmark numerical optimization problems, constrained and unconstrained, are presented here to demonstrate the effectiveness and robustness of the proposed Hunting Search (HuS) algorithm. The results indicate that the proposed method is a powerful search and optimization technique. It yields better solutions compared to those obtained by some current algorithms when applied to continuous problems.
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ISSN:0898-1221
1873-7668
DOI:10.1016/j.camwa.2010.07.049