Dung beetle optimization algorithm based on quantum computing and multi-strategy fusion for solving engineering problems

Abstract

The Dung beetle optimization algorithm is a kind of group intelligence optimization algorithm proposed by Jiankai Xue in 2022, which has the characteristics of strong optimization-seeking ability and fast convergence but suffers from the defect of easily falling into local optimum at the late stage of optimization-seeking as other group intelligence optimization algorithms. To address this problem, this paper proposes a dung beetle search algorithm (QHDBO) based on quantum computing and a multi-strategy hybrid. The good point set strategy is used to initialize the initial population of dung beetles . That makes the initial population more evenly distributed, and reduces the likelihood of the algorithm falling into a local optimum solution. The convergence factor and dynamic balance between the number of Spawning and foraging dung beetles is proposed. That allows the algorithm to focus on the global search in the early stages and local exploration in the later stages. The quantum computing based t-distribution variation strategy is used to variate the optimal global solution, that prevents the algorithm from falling into a local optimum. To verify the performance of the QHDBO algorithm, this paper compares QHDBO with six other swarm intelligence algorithms through 37 test functions and practical engineering application problems. The experimental results show that the improved dung beetle optimization algorithm significantly improves convergence speed and optimization accuracy and has good robustness.