In the traditional quantum wolf pack al-gorithm,the wolf pack distribution is simplified,and the leader wolf is randomly selected.This leads to the prob-lems that the development and exploration ability of the algorithm is weak and the rate of convergence is slow.Therefore,a quantum wolf pack evolutionary algorithm of weight decision-making based on fuzzy control is pro-posed in this paper.First,to realize the diversification of wolf pack distribution and the regular selection of the leader wolf,a dual strategy method and sliding mode cross principle are adopted to optimize the selection of the quantum wolf pack initial position and the candidate leader wolf.Second,a new non-linear convergence factor is adopted to improve the leader wolf's search direction operator to enhance the local search capability of the al-gorithm.Meanwhile,a weighted decision-making strategy based on fuzzy control and the quantum evolution com-putation method is used to update the position of the wolf pack and enhance the optimization ability of the al-gorithm.Then,a functional analysis method is adopted to prove the convergence of the quantum wolf pack al-gorithm,thus realizing the feasibility of the algorithm's global convergence.The performance of the quantum wolf pack algorithm of weighted decision-making based on fuzzy control was verified through six standard test func-tions.The optimization results are compared with the standard wolf pack algorithm and the quantum wolf pack algorithm.Results show that the improved algorithm had a faster rate of convergence,higher convergence precision,and stronger development and exploration ability.

LU Na;MA Long

School of Economics and Management,Xi'an Aeronautical University,Xi'an 710077,China

电子学报（英文）

[1]LU Na;MA Long-.Quantum Wolf Pack Evolutionary Algorithm of Weight Decision-Making Based on Fuzzy Control)[J].电子学报（英文）,2022(04):635-646

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