[1]MA Long,LU Caiwu,GU Qinghua.Cellular and quantum-behaved wolf pack evolutionary algorithm for solving discrete optimization problems[J].CAAI Transactions on Intelligent Systems,2018,13(5):716-727.[doi:10.11992/tis.201705007]
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Cellular and quantum-behaved wolf pack evolutionary algorithm for solving discrete optimization problems

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 5 Page number: 716-727 Column: 学术论文—机器学习 Public date: 2018-09-05
Title:
Cellular and quantum-behaved wolf pack evolutionary algorithm for solving discrete optimization problems
Author(s):
MA Long;  LU Caiwu;  GU Qinghua
School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
Keywords:
discrete optimization; quantum-inspired wolf pack algorithm; cellular automata; double strategy method; sliding mode crossover; binary encoding; functional analysis; wolf pack algorithm; quantum rotation angle
CLC:
TP301.6
DOI:
10.11992/tis.201705007
Abstract:
To solve optimization problems in discrete space, a cellular quantum-inspired wolf pack evolutionary algorithm is proposed for solving discrete optimization problems. First, to speed up the global convergence of the algorithm, when generating the diversity of population, the method fully utilizes the double strategy quantum bit initialization method and the sliding mode crossover method to help generate the initial position and candidate wolf, respectively. Then, to accurately describe the distance between the wolf and the prey as well as enhance the traverse range of wolf pack, the methods of the binary encoding and evolution rules of the cellular automata are used to realize precise description and the selection of the quantum rotation angle, respectively. Then to prove the convergence performance of the algorithm, the method fully utilizes the functional analysis to verify the global convergence. Finally, simulation experiment on six benchmark functions was carried out, and the comparison between the wolf pack algorithm and quantum-inspired wolf pack evolutionary algorithm was provided. The results show that the proposed approach has better convergence speed and great global convergence optimization ability.
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