An improved wolf search algorithm introduced by Nelder-Mead operator

In order to test the performance of the proposed algorithm can find out better optimum, has a stronger robustness, and it can be used to solve the high dimensional and complex optimization problems. In order to overcome the shortcomings of wolf searching algorithm (WSA), a new hybrid optimization algorithm is proposed, which is called the improved wolf searching algorithm which introduces Nelder-Mead operator. This algorithm enables each wolf to use the group information and individual memory to guide its search for prey in the individual search in order to improve the global search ability of the algorithm and let each wolf use the Nelder-Mead method in the individual search to make up for the WSA Lack of local search capabilities. Six benchmark functions are chosen to test the optimal performance of the algorithm. Experimental results show that this algorithm can find a better optimal solution and is more robust and can be applied to solving high-dimensional complex optimization problems.