Hunting Optimization: An New Framework for Single Objective Optimization Problems

Abstract

Swarm intelligence algorithms play vital roles in objective optimization problems. To solve diverse and increasingly complicated problems, a new algorithm is always desired. This paper proposes a new optimization algorithm named hunting optimization based on human hunting activities. The population has consisted of huntsmen and hunting dogs. Each of them represents a feasible solution. In the evolution process, each huntsman shrinks its hunting ground with an adaptive reduction factor to concentrate on searching the most promising area. Then, each huntsman uses its dogs to search its local hunting ground and updates its position to a more promising place by its own searching results as well as the results of other huntsmen. At the same time, to further balance the exploration and exploitation, huntsmen with the least prey will be eliminated and their dogs will be distributed to others. Congestion detection is also applied to avoid getting stuck at a local optimum. The experimental results on 12 benchmark functions and CEC2013 test suites compared with 12 state-of-the-art algorithms demonstrate the effectiveness of the proposed method.

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“…Some recent techniques that can be used to solve this kind of problems are the Jaya algorithm [33], the Proactive Particles in Swarm Optimization (PPSO) [34], the Hybrid Population-Based Mean-Variance Mapping Optimization (MVMO-PH) [35], the Successful-History based Adaptative Differential Evolution with Linear size reduction and four adaptation strategies with Improved ε constraint-handling method (LSHADE44-IEpsilon) [36], the Hunting Optimization (HO) [37], and the Modified Adaptative Bats Sonar Algorithm with Doppler Effect (MABSA-DE) [38].…”