闫旭, 叶春明. 混合随机量子鲸鱼优化算法求解TSP问题[J]. 微电子学与计算机, 2018, 35(8): 1-5, 10.
引用本文: 闫旭, 叶春明. 混合随机量子鲸鱼优化算法求解TSP问题[J]. 微电子学与计算机, 2018, 35(8): 1-5, 10.
YAN Xu, YE Chun-ming. Hybrid Stochastic Quantum Whale Optimization Algorithm Solving Travelling Salesman Problem[J]. Microelectronics & Computer, 2018, 35(8): 1-5, 10.
Citation: YAN Xu, YE Chun-ming. Hybrid Stochastic Quantum Whale Optimization Algorithm Solving Travelling Salesman Problem[J]. Microelectronics & Computer, 2018, 35(8): 1-5, 10.

混合随机量子鲸鱼优化算法求解TSP问题

Hybrid Stochastic Quantum Whale Optimization Algorithm Solving Travelling Salesman Problem

  • 摘要: 为克服基本鲸鱼优化算法(WOA)解决TSP问题时收敛精度低、容易陷入局部最优的缺陷, 本文借鉴量子计算思想提出了四种算法改进方案, 并进行了基于TSP标准测试实例的仿真实验及与文献中其他算法的对比分析.研究发现, 在解决TSP问题时混合随机量子鲸鱼优化算法(HSQWOA)收敛精度更高、全局搜索能力更强, 能够跳出局部最优, 具有更加优越的性能.

     

    Abstract: To overcome whale optimization algorithm's disadvantages of poor convergence and being easily trapped in local optima in solving travelling salesman problem, this paper proposed4 schemes to improve the algorithmbased on the idea of quantum computation. Then this paper providedcomparative analysis ondifferent schemes and algorithmsbased on a set of TSP benchmark instances. Simulation results show thatthe proposed hybrid stochastic quantum whale optimization algorithm(HSQWOA) has the merits of higher convergence accuracy, higher local optima avoidance and better exploration insolving travelling salesman problem.

     

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