量子群智能优化算法综述  

Survey of Quantum Swarm Intelligence Optimization Algorithm

作　　者：安家乐 刘晓楠[1] 何明 宋慧超 AN Jiale;LIU Xiaonan;HE Ming;SONG Huichao(State Key Laboratory of Mathematical Engineering and Advanced Computing,Information Engineering University,Zhengzhou 450000,China)

机构地区：[1]信息工程大学数学工程与先进计算国家重点实验室,郑州450000

出　　处：《计算机工程与应用》2022年第7期31-42,共12页Computer Engineering and Applications

基　　金：国家超算郑州中心创新生态系统建设专项(201400210200);国家自然科学基金(61972413,61701539)。

摘　　要：随着科学技术的不断发展,最优化理论及其衍生出的算法已经广泛应用于人们的日常工作与生活当中,现实世界中的很多问题都可以被描述为组合优化问题。群智能优化算法这些年来被证明在解决组合优化问题方面效果显著,将当下处于研究热点的量子计算概念引入群智能优化算法形成的量子群智能优化算法,为更好地解决组合优化问题提出了一个新的研究方向。在过去的二十多年里,许多量子群智能优化算法被不断开发出来,同时在此基础上进行了大量改进与应用。综述了量子蚁群算法、量子粒子群算法、量子人工鱼群算法、量子人工蜂群算法、量子布谷鸟搜索算法、量子混合蛙跳算法、量子萤火虫算法、量子蝙蝠算法等量子群智能优化算法,并对量子群智能优化算法面临的问题以及未来研究方向进行了深入探讨。With the continuous development of science and technology, optimization theory and its derived algorithms have been widely used in people’s daily work and life, especially in the real world, many problems can be described as combinatorial optimization problems. In recent years, swarm intelligence optimization algorithm has been proved to be effective in solving combinatorial optimization problems. Introducing the concept of quantum computing into swarm intelligence optimization algorithm, a new research direction is opened for better solving combinatorial optimization problems. In the past 20 years, many quantum swarm optimization algorithms have been developed, and more people have improved and applied them. This paper summarizes quantum ant colony algorithm, quantum particle swarm algorithm,quantum artificial fish swarm algorithm, quantum bee colony optimization algorithm, quantum cuckoo search algorithm,quantum hybrid frog leaping algorithm, quantum firefly algorithm, quantum bat algorithm and other quantum swarm Intelligent optimization algorithms. The future problems and research directions of quantum swarm optimization algorithm are discussed.

关 键 词：量子计算 群智能优化算法 量子蚁群算法 量子群智能优化算法 组合优化 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]