An improved wolf pack algorithm

Abstract

This paper proposes an improved wolf pack algorithm (IWPA) to overcome the shortcomings of slow convergence speed, easy to fall into local optimum, single artificial wolf optimization method and unsatisfactory interaction. In the framework of cultural algorithm, the algorithm integrates the adaptive wolf pack optimization algorithm into the population space, and proposes a prey allocation method based on inverse allocation. The two population spaces can evolve independently and in parallel, and realize interactive learning at an appropriate time to promote the evolution of the whole population, so as to improve the global optimization ability of IWPA algorithm. The purpose of improving the precision of optimization. The simulation results show that the improved wolf pack algorithm has higher solution accuracy and convergence speed than WPA algorithm.