Hunting Optimization: An New Framework for Single Objective Optimization Problems
Zhenchong Zhao, Xiaodan Wang, Chongming Wu, Lei Lei · IEEE Access · 2019
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.