An Adaptive Cooperative Dual Particle Swarm Optimization Algorithm with Chaotic Mutation and Quantum Behavior

doi:10.23940/ijpe.19.10.p9.26362644

Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (10): 2636-2644.doi: 10.23940/ijpe.19.10.p9.26362644

• Orginal Article • Previous Articles Next Articles

An Adaptive Cooperative Dual Particle Swarm Optimization Algorithm with Chaotic Mutation and Quantum Behavior

Tianfei Chen^a^b, Lijun Sun^a^b^*, Xiaodong Song^a^b, and Haixu Niu^c

^aSchool of Electrial Engineering, Henan University of Technology, Zhengzhou, 450001, China
^bZhengzhou Key Laboratory of Machine Perception and Intelligence Systems, Zhengzhou, 450001, China
^cBelarusian State Pedagogical University, Minsk, 220030, Belarus

Submitted on ; Revised on ; Accepted on
Contact: Sun Lijun
About author:
* Corresponding author.E-mail address: sunlijunzz@163.com
Supported by:
Fund The authors sincerely thank the editors and reviewers for their valuable suggestions and useful comments that have led to the present improved version of the original manuscript This work was supported in part by the National Natural Science Foundation of China (No U1604151, 61803146), Outstanding Talent Project of Science and Technology Innovation in Henan Province (No 174200510008), Science and Technology Project of Henan Province (No 182102210094), Natural Science Project of Education Department of Henan Province (No 18A510001), and Fundamental Research Funds of Henan University of Technology (No 2015QNJH13, 2016XTCX06)

Abstract

Abstract:

An adaptive cooperative dual particle swarm optimization algorithm with chaotic mutation and quantum behavior is proposed to solve the contradiction between global search and local refinement search for basic particle swarm optimization algorithms. The strategy of adaptive cooperative evolution for two subgroups is used to parallel search, the subgroup with the chaotic mutation operator modifies the historical optimal position of particles and the subgroup optimal position using the principle of chaotic randomly ergodicity, and the chaotic mutation radius is increasing with the iterative evolution to enhance the global search ability. Additionally, in order to improve the local refinement search ability, the subgroup with quantum behavior, which casts off the searching orbital, updates the average optimal position of the subgroup and the subgroup optimal position during evolution. Finally, the numerical simulation results demonstrate that the proposed algorithm not only has fast convergence speed and high convergence accuracy, but also has significant advantages in dimension expansion.

Key words: particle swarm optimization, chaotic mutation, quantum behavior, adaptive cooperative

Tianfei Chen, Lijun Sun, Xiaodong Song, and Haixu Niu. An Adaptive Cooperative Dual Particle Swarm Optimization Algorithm with Chaotic Mutation and Quantum Behavior [J]. Int J Performability Eng, 2019, 15(10): 2636-2644.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References 13.

1.	R. C.Eberhart and J. Kennedy, “A New Optimizer using Particle Swarm Theory,” inProceedings of International Symposium on Micro Machine and Human Science, pp. 39-43, 2002
2.	Y. Shi and R. C. Eberhart, “Empirical Study of Particle Swarm Optimization,”IEEE Congress on Evolutionary Computation, Vol. 3, pp. 1945-1950, 1999
3.	Y. T. Juang, S. L. Tung,H. C. Chiu, “Adaptive Fuzzy Particle Swarm Optimization for Global Optimization of Multimodal Functions,” Information Sciences, Vol. 181, No. 20, pp. 4539-4549, 2011
4.	B. Alatas, E. Akin,A. B. Ozer, “Chaos Embedded Particle Swarm Optimization Algorithms,” Chaos Solitons and Fractals, Vol. 40, No. 4, pp. 1715-1734, 2009
5.	P. J. Angeline, “Using Selection to Improve Particle Swarm Optimization,” inProceedings of IEEE International Conference on Evolutionary Computation, pp. 84-89, 1998
6.	B. Xin, J. Chen, Z. H. Peng,F. Pan, “An Adaptive Hybrid Optimizer based on Particle Swarm and Differential Evolution for Global Optimization,” Science China (Information Sciences), Vol. 53, No. 5, pp. 980-989, 2010
7.	F. Han and Q. Liu, “A Diversity-Guided Hybrid Particle Swarm Optimization,” Neurocomputing, Vol. 137, No. 4, pp. 234-240, 2014
8.	J. Liang, K. Qin, P. N. Suganthan,B. Subramanian, “Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions,” IEEE Transactions on Evolutionary Computation, Vol. 10, No. 3, pp. 281-295, 2006
9.	J. Y. Chen, Z. Qin, Y. Liu,J. Lu, “Particle Swarm Optimization with Local Search,” inProceedings of International Conference on Neural Networks and Brain, pp. 481-484, 2005
10.	X. W. Xia, J. N. Liu, K. F. Gao, Y. X. Li,H. Zeng, “Particle Swarm Optimization Algorithm with Reverse-Learning and Local-Learning Behavior,” Journal of Software, Vol. 38, No. 7, pp. 1397-1407, 2015
11.	H. Wang, H. B. Ouyang,L. Q. Gao, “An Improved Global Particle Swarm Optimization,” Control and Decision, Vol. 31, No. 7, pp. 1161-1168, 2016
12.	X. L. Jin, L. H. Ma, T. J. Wu,J. X. Qian, “Convergence Analysis of the Particle Swarm Optimization based on Stochastic Processes,” Acta Automatica Sinica, Vol. 33, No. 12, pp. 1263-1268, 2007
13.	W. Fang, J. Sun, Y. R. Ding, X. J. Wu,W. B. Xu, “A Review of Quantum-Behaved Particle Swarm Optimization,” IETE Technical Review, Vol. 27, No. 4, pp. 336-348, 2010

[1]	D.P. Tripathi, Mahesh Nayak, Rajaboina Manoj, Surarapu Sudheer, and K. Praghash. Fast Computational Efficient Directional Shrinking Search Optimization Algorithm [J]. Int J Performability Eng, 2021, 17(6): 543-551.
[2]	Bouzouada Abdallah, Yssaad Benyssaad, Daoud Mohamed, Bekkouche Benaissa, and Yagoubi Benabdellah. Maintenance Optimization for Complex System using Evolutionary Algorithms under Reliability Constraints within the Context of the Reliability-Centered-Maintenance [J]. Int J Performability Eng, 2021, 17(1): 1-13.
[3]	Kun Li, Liwei Jia, and Xiaoming Shi. IPSOMC: An Improved Particle Swarm Optimization and Membrane Computing based Algorithm for Cloud Computing [J]. Int J Performability Eng, 2021, 17(1): 135-142.
[4]	Xiu Kan, Xiafeng Zhang, Le Cao, Dan Yang, and Yixuan Fan. EMG Pattern Recognition based on Particle Swarm Optimization and Recurrent Neural Network [J]. Int J Performability Eng, 2020, 16(9): 1404-1415.
[5]	Shenyi Qian, Yongsheng Shi, Huaiguang Wu, and Songtao Shang. Prediction of Electricity Tariff Recovery Risk based on Hybrid Feature Selection Algorithm [J]. Int J Performability Eng, 2020, 16(6): 846-854.
[6]	Guochao Ding, Haosong Duan, Xi Wang, and Zhen Zhou. Analysis of Extinction Spectrum Apparent Property and Extraction of Specific Wavelength of Milk Fat Particles in Liquid Absorbing Medium [J]. Int J Performability Eng, 2020, 16(3): 430-437.
[7]	Youfen Chen. Improved Particle Swarm Optimization Algorithm for Image Segmentation [J]. Int J Performability Eng, 2020, 16(3): 482-489.
[8]	Roshan A. Gangurde and Binod Kumar. Next Web Page Prediction using Genetic Algorithm and Feed Forward Association Rule based on Web-Log Features [J]. Int J Performability Eng, 2020, 16(1): 10-18.
[9]	Manu Banga, Abhay Bansal, and Archana Singh. Proposed Hybrid Approach to Predict Software Fault Detection [J]. Int J Performability Eng, 2019, 15(8): 2049-2061.
[10]	Sha Li and Yu Quan. Financial Risk Prediction for Listed Companies using IPSO-BP Neural Network [J]. Int J Performability Eng, 2019, 15(4): 1209-1219.
[11]	Ming Lei, Bin Wen, Jianhou Gan, and Jun Wang. Clustering Algorithm of Ethnic Cultural Resources based on Spark [J]. Int J Performability Eng, 2019, 15(3): 756-762.
[12]	Guangzhou Yu. Database Repeat Record Detection based on Improved Quantum Particle Swarm Optimization Algorithm [J]. Int J Performability Eng, 2019, 15(2): 710-718.
[13]	Gaurav Sharma and P. C. Tewari. Performability Analysis for Feeding Unit of a Sugar Plant using Particle Swarm Optimization Technique [J]. Int J Performability Eng, 2019, 15(11): 2835-2842.
[14]	Wenfang Zhao, Jingli Wang, Dongchang Yu, and Ge Zhang. Prediction of Daily Pollen Concentration using Support Vector Machine and Particle Swarm Optimization Algorithm [J]. Int J Performability Eng, 2018, 14(11): 2808-2819.

An Adaptive Cooperative Dual Particle Swarm Optimization Algorithm with Chaotic Mutation and Quantum Behavior

PDF

Knowledge

Abstract

Cite this article

share this article

References 13.

Related Articles 14

Recommended 0