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混沌自适应量子萤火虫算法 被引量:1

Chaotic Adaptive Quantum Firefly Algorithm
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摘要 为提升量子萤火虫算法(Quantum Firefly Algorithm,QFA)的搜索性能,解决其在面对部分问题时易陷入局部最优等问题,文中提出了一种引入混沌映射、邻域搜索以及自适应随机扰动的改进量子萤火虫算法——混沌自适应量子萤火虫算法(Chaotic Adaptive Quantum Firefly Algorithm,CAQFA)。该算法将混沌映射应用于种群的初始化阶段,提高初始种群的质量;并在更新阶段对当前种群中的最优个体进行邻域搜索,增强算法跳出局部最优的能力;对其他个体引入自适应的随机扰动,增加算法的随机性,在对搜索空间的探索和开发之间寻找平衡,以此提升算法的性能。文中选取了18个不同类型的基准函数对算法的性能进行测试,并将其与萤火虫算法(Firefly Algorithm,FA)、QFA以及量子粒子群优化(Quantum Particle Swarm Optimization,QPSO)算法进行对比。实验结果表明,CAQFA具有更好的搜索能力和稳定性,表现出了较强的竞争力。 In order to improve the search performance of quantum firefly algorithm(QFA)and solve the problem that it is easy to fall into local optimality when facing some problems,an improved QFA with chaotic map,neighborhood search and adaptive random disturbance is proposed,named chaos adaptive quantum firefly algorithm(CAQFA).In this algorithm,chaotic map is applied to the initialization stage of the population to improve the quality of the initial population.In the update stage,the neighborhood search is carried out for the optimal individual of the current population to enhance the ability of the algorithm to jump out of the local optimization.The introduction of adaptive random disturbance to other individuals increases the randomness of the algorithm and achieves a balance between the exploration and development of search space,so as to improve the performance of the algorithm.Eighteen different types of benchmark functions are selected to test the performance of the algorithm.The test results show that CAQFA has better search ability,stability and strong competitiveness compared with firefly algorithm(FA),QFA and quantum particle swarm optimization(QPSO).
作者 刘晓楠 安家乐 何明 宋慧超 LIU Xiaonan;AN Jiale;HE Ming;SONG Huichao(State Key Laboratory of Mathematical Engineering and Advanced Computing,PLA Information Engineering University,Zhengzhou 450000,China)
出处 《计算机科学》 CSCD 北大核心 2023年第4期204-211,共8页 Computer Science
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)。
关键词 量子萤火虫算法 群体智能 全局优化 混沌映射 测试函数 Quantum firefly algorithm Swarm intelligence Global optimization Chaotic map Test functions
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  • 1张双明,赵光明,查文华.鸡笼山金矿天轮硐室施工技术[J].建井技术,2004,25(6):16-19. 被引量:2
  • 2韩立军,王延宁,周胜利,董亚宁.软弱岩层中大断面硐室施工与支护技术研究[J].金属矿山,2006,35(11):23-26. 被引量:19
  • 3罗述谦,周果宏.医学图像处理与分析[M].北京:科学出版社,2010:164-165. 被引量:12
  • 4章毓晋.图像工程(中册)-图像分析[M].北京:清华大学出版社,2012,73-201. 被引量:4
  • 5KAPUR J N. A new method for gray-level picture thresholding using the entropy of the histogram [J]. Computer Vision, Graphics, and Image Pro cessing, 1985, 29(3):273-285. 被引量:1
  • 6BRINK A D. Thresholding of digital images using Two-dimensional entropies [J].Patlern Recogni- tion, 1992, 25(8): 803-808. 被引量:1
  • 7PEDRAMG, M1CAELSC, JONAB, eta&. Anef ficient method for segmentation of images based on fractional calculus and natural selecfion[J]. Erpert Systems uitk AppZications, 2012, 89: 12407-12417. 被引量:1
  • 8HORNG M H. Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization [J]. E.cpert System with Applica- tions, 2010, 37(6):4580-4592. 被引量:1
  • 9I.AN J H,ZENG Y L. Multi-threshold image seg- mentation using maximum fuzzy entropy based on a new 2D histogram [J]. Optilelnt. J. Ligkt Elec- tron Opt. , 2013,124(18):3756-3760. 被引量:1
  • 10YANG X SH. Firefly algorithms for multimodal optimization [C]. In Stochastic Algorithms Foun- dations and Applications, SAGA 2009, Lecture Notes in Computer Sciences, 2009, 5792:169-178. 被引量:1

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