量子竞争决策算法及其在旅行商问题中的应用*

提出一种新型优化算法——量子竞争决策算法，在竞争决策的基础上，将进化博弈论中博弈者不断学习和调整来提高竞争力的思想引入到优化中，使竞争者具有自进化能力，同时充分利用量子进化计算中量子比特、叠加态等理论，增加竞争群体的多样性，缩小群体规模。通过对典型的TSP实验计算和与其他算法比较，均取得了较好的效果，算法具有较强的全局优化能力。

Quantum competitive decision algorithm and its application in TSP

This paper proposed a novel optimization algorithm-quantum competitive decision algorithm.Based on competition and decision,the algorithm introduced the theory of continuous learning and adjustment to improve the competitiveness in evolutionary game theory into optimization,making competitors possess the ability of self-optimizing.The algorithm made full use of quantum bit,superposition state and other concepts in quantum evolutionary algorithm to increase the diversity of competitors and reduce the population size.Experiments on typical TSP and comparisons with other methods show the new algorithm is more efficient and the algorithm has strong capability of global optimization.