On how pachycondyla apicalis ants suggest a new search algorithm
Future Generation Computer Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic AlgorithmsNumerical Optimizationand Constraints
Proceedings of the 6th International Conference on Genetic Algorithms
The Ant Colony Metaphor for Searching Continuous Design Spaces
Selected Papers from AISB Workshop on Evolutionary Computing
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Continuous interacting ant colony algorithm based on dense heterarchy
Future Generation Computer Systems - Special issue: Computational chemistry and molecular dynamics
Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
Mixed variable structural optimization using Firefly Algorithm
Computers and Structures
A novel meta-heuristic optimization algorithm: current search
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Hunting search algorithm based design optimization of steel cellular beams
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Bat algorithm with recollection
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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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.