一种混沌蚁群算法的多峰函数优化方法

doi:10.3969/j.issn.1001-2400.2015.03.024

Abstract

Abstract:

In order to quickly and accurately obtain the global and local peaks of multimodal functions, this paper proposes an optimization method for multimodal functions based on the henon chaotic ant colony algorithm. In this method, the numerical characters composed by numerical solutions of complex functions are turned into the nodes of the city distribution network on the ants searching paths, the ant colonies whose number is the same as the number of function variables are constructed to solve the problem by global search, and chaos mapping technology is used to update the pheromones on the ant colony optimization path adaptively. The solving performance of this optimization method is verified by adopting low-dimensional and high-dimensional benchmark test functions and compared with those of gravitational search algorithms and other methods in literature. The comparison results indicate that, in low-dimensional multimodal function optimizations, the search efficiency of this method is two times higher than that of other methods in literature, when the number of dimensions of high-dimensional functions is greater than five, the optimization efficiency of this method is basically the same as that of other methods. However, the ability of obtaining all the global and local solutions and the solving accuracy are much higher than those of other methods.

Key words: chaos, ant colony algorithm, multimodal function, optimization, self-adaptive adjust tactics

CLC Number:

TP202+.7

LIU Daohua;NI Yongjun;SUN Fang;ZHANG Fei. Optimization method for multimodal functions based on chaotic ant colony algorithms[J].J4, 2015, 42(3): 141-147.

References

［1］ Li Minqiang, Lin Dan, Kou Jisong. A Hybrid Niching PSO Enhanced with Recombination-replacement Crowding Strategy for Multimodal Function Optimization［J］. Applied Soft Computing, 2012, 12(3): 975-987.
［2］ Jovani L F, Luiz F L R, Paulo L C. Comparison of Methods for Multivariate Moment Inversion—Introducing the Independent Component Analysis［J］. Computers ＆ Chemical Engineering, 2014, 60: 41-56.
［3］ Subhrajit R, Minhazul S K, Swagatam D, et al. Multimodal Optimization by Artificial Weed Colonies Enhanced with Localized Group Search Optimizers［J］. Applied Soft Computing, 2013, 13(1): 27-46.
［4］ Antanas Z. On Strong Homogeneity of Two Global Optimization Algorithms Based on Statistical Models of Multimodal Objective Functions［J］. Applied Mathematics ＆ Computation, 2012, 218(16): 8131-8136.
［5］ Shima K. Using a Self-adaptive Neighborhood Scheme with Crowding Replacement Memory in Genetic Algorithm for Multimodal Optimization［J］. Swarm and Evolutionary Computation, 2013, 12: 1-17.
［6］刘道华, 张礼涛, 曾召霞, 等. 基于正交最小二乘法的径向基神经网络模型［J］. 信阳师范学院学报, 2013, 26(3): 428-431.
Liu Daohua, Zhang Litao, Zeng Zhaoxia, et al. Radial Basis Function Neural Network Model Based on Orthogonal Least Squares［J］. Journal of Xinyang Normal University, 2013, 26(3): 428-431.
［7］李文启, 仇一鸣, 汪镭, 等. 基于NichePSO和网格模型的参数优化方法及仿真［J］. 系统仿真学报, 2013, 25(6): 1231-1236.
Li Wenqi, Qiu Yiming, Wang Lei, et al. NichePSO and Grid Model Based Parameter Optimization Method and Its Simulation［J］. Journal of System Simulation, 2013, 25(6): 1231-1236.
［8］吴建辉, 章兢, 陈华, 等. 多峰函数优化的免疫云粒子群优化算法［J］. 仪器仪表学报, 2013, 34(8): 1756-1764.
Wu Jianhui, Zhang Jing, Chen Hua, et al. Immune Cloud Particle Swarm Optimization Algorithm for Multimodal Function Optimization［J］. Chinese Journal of Scientific Instrument, 2013, 34(8): 1756-1764.
［9］刘星宝, 蔡自兴, 王勇. 用于全局优化问题的混合免疫进化算法［J］. 西安电子科技大学学报, 2010, 37(5): 772-776.
Liu Xingbao, Cai Zixing, Wang Yong. Hybrid Immune Evolutionary Algorithm for Global Optimization Problems［J］. Journal of Xidian University, 2010, 37(5): 772-776.
［10］刘合安, 王雷. 粒子群Memetic算法求解多峰函数优化［J］. 计算机工程与应用, 2012, 48(22): 10-13.
Liu Hean, Wang Lei. Multi-peak Function Optimization Based on Particle Swarm Optimization Memetic Algorithm［J］. Computer Engineering and Applications, 2012, 48(22): 10-13.
［11］ Agata C, Anna N, Mikolaj R. Sensitivity Analysis of Mathematical Models of Signaling Pathways［J］. Journal of Biotechnology,Computational Biology and Bionanotechnology, 2012, 93(3): 291-308.
［12］原思聪, 刘道华, 江祥奎, 等. 基于蚁群算法的多维有约束函数优化研究［J］. 计算机应用研究, 2008, 25(6): 1682-1684.
Yuan Sicong, Liu Daohua, Jiang Xiangkui, et al. Research on Multi-dimension and Constrained Function Optimization Based on ACA［J］. Application Research of Computers, 2008, 25(6): 1682-1684.

[1]	GE Bin,CHEN Xu,CHEN Gang. Fast hyper-chaotic image encryption algorithm using vector operation [J]. Journal of Xidian University, 2021, 48(6): 187-196.
[2]	ZHANG Yupeng,WU Zili,CHEN Ming,ZHANG Lulu. Optimization of task scheduling oriented to cross microservice chains [J]. Journal of Xidian University, 2021, 48(6): 32-39.
[3]	LI Peng,FENG Cunqian,XU Xuguang,TANG Zixiang. Ballistic target fretting classification network based on Bayesian optimization [J]. Journal of Xidian University, 2021, 48(5): 139-148.
[4]	DU Wenzhan,YU Zhiyong,YANG Jian,JIANG Haibin. Improved NSGA-II algorithm and research on monitoring antenna optimization deployment [J]. Journal of Xidian University, 2021, 48(5): 239-248.
[5]	MA Yue,ZHANG Yumei. Method for distributed deployment of the virtual network function manager for MEC [J]. Journal of Xidian University, 2021, 48(4): 20-26.
[6]	YAN Qunmin,MA Ruiqing,MA Yongxiang,WANG Junjie. Adaptive simulated annealing particle swarm optimization algorithm [J]. Journal of Xidian University, 2021, 48(4): 120-127.
[7]	LI Ming,HU Jiangping,CAO Xiaoli. Minimum cost of node deployment strategy for heterogeneous sensor networks [J]. Journal of Xidian University, 2021, 48(4): 11-19.
[8]	YANG Zhongping,ZHOU Qingsong,ZHANG Jianyun. Optimization algorithm for ultra-sparse arrays in regional energy focusing [J]. Journal of Xidian University, 2021, 48(4): 57-63.
[9]	LIU Tianyu,WANG Zhu. Diversity controlled multiobjective particle swarm optimization [J]. Journal of Xidian University, 2021, 48(3): 106-114.
[10]	LIU Didi,SUN Haotian,XIAO Jiawen,JIANG Frank,ZHENG Kunkun. Two-way energy trading algorithms for the end-user in the smart grid [J]. Journal of Xidian University, 2021, 48(3): 131-137.
[11]	HE Shiyang,LI Hui,LI Fenghua. Optimization and implementation of the SM4 on FPGA [J]. Journal of Xidian University, 2021, 48(3): 155-162.
[12]	YANG Wusi,CHEN Li,WANG Yi,ZHANG Maosheng. Many-objective particle swarm optimization algorithm for fitness ranking [J]. Journal of Xidian University, 2021, 48(3): 78-84.
[13]	LI Teng,CAO Shijie,YIN Siwei,WEI Dawei,MA Xindi,MA Jianfeng. Optimal method for the generation of the attack path based on the Q-learning decision [J]. Journal of Xidian University, 2021, 48(1): 160-167.
[14]	ZHANG Gang,LIU Jinhui,ZHANG Peng. Design and analysis of an improved OMU-NRDCSK communication system [J]. Journal of Xidian University, 2020, 47(4): 1-9.
[15]	LOU Lifei,ZHAO Jianxin,LIANG Ya’nan,ZHAO Mingyang,AN Zaifang. Design and optimization of the piezoelectric micromechanical ultrasonic transducer with an AlN thin film [J]. Journal of Xidian University, 2020, 47(3): 8-13.

Optimization method for multimodal functions based on chaotic ant colony algorithms

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0