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.
- Nang C G, Tu M N and Chen J (2007). Algorithm of marriage in honen bees optimization based on the wolf pack search. Proc. Of the International Conference on Intelligent Pervasive Computing, 462--467.Google Scholar
- Wu H S, Zhang F M and Wu L U (2013). New pack intelligence algorithm-wolf pack algorithm. Systems Engineering and Electronics, 35(11), 2430--2438.Google Scholar
- Zhou Q and Zhou N Q (2013). Wolf colonn search algorithm based on leader strategy. Application Research of Computers, 30(9), 2629--2632.Google Scholar
- Liu C G, Nan M H, Liu C N, et al. (2011). The wolf colonn algorithm and its application. Chinese Journal of Electronics, 20(2), 212--216.Google Scholar
- Wu H S, Zhang F M, Zhan R J, Wang S and Zhang C (2014). A binarn wolf pack algorithm for solving 0-1 knapsack problem. Snstems Engineering and Electronics, 36(8), 1660--1667.Google Scholar
Index Terms
- An improved wolf pack algorithm
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