自适应杂交退火粒子群优化算法

doi:10.12305/j.issn.1001-506X.2022.11.22

Abstract

Abstract:

To avoid premature convergence and improve its speed and accuracy of the particle swarm optimization (PSO) algorithm, an adaptive hybrid annealing PSO algorithm is proposed. A Sigmoid function is used to control the inertia weight to balance its global and local optimization capability. A hyperbolic tangent function is applied to control the acceleration coefficients to balance the self and social cognition capability of the proposed algorithm to improve its accuracy. A simulated annealing operator is used to ensure the capability of the proposed algorithm to jump out from the local optimal solution. At the last stage of the algorithm, a hybrid variation operator is used to increase its population diversity, hence further improving its accuracy. The performance of the proposed algorithm is verified based on three standard test functions and compared with typical PSO algorithms. The results show that the proposed algorithm has a great improvement in accuracy and convergence speed. Finally, the proposed algorithm is applied to array pattern synthesis, showing a better performance than existing algorithms.

Key words: adaptive particle swarm optimization (PSO), simulated annealing, hybrid variation, array pattern synthesis

CLC Number:

TP301.6

Fuyu LU, Ningning TONG, Weike FENG, Pengcheng WAN. Adaptive hybrid annealing particle swarm optimization algorithm[J]. Systems Engineering and Electronics, 2022, 44(11): 3470-3476.

Figures/Tables 8

Table 1

Fig.1

Table 2

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

References 30

1	KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proc. of the IEEE International Conference on Neural Networks, 1995: 1942-1948.
2	WANG D S , TAN D P , LIU L . Particle swarm optimization algorithm: an overview[J]. Soft Computing, 2018, 22 (2): 387- 408. doi: 10.1007/s00500-016-2474-6
3	MISTRY K , ZHANG L , NEOH S C , et al. A micro-GA embedded PSO feature selection approach to intelligent facial emotion recognition[J]. IEEE Trans.on Cybernetics, 2016, 47 (6): 1496- 1509.
4	MELO H , WATADA J . Gaussian-PSO with fuzzy reasoning based on structural learning for training a neural network[J]. Neurocomputing, 2016, 172, 405- 412. doi: 10.1016/j.neucom.2015.03.104
5	SONG B Y , WANG Z D , ZOU L . On global smooth path planning for mobile robots using a novel multimodal delayed PSO algorithm[J]. Cognitive Computation, 2017, 9 (1): 5- 17. doi: 10.1007/s12559-016-9442-4
6	ZHAO S Q , ZENG D G , WANG W H , et al. Mutation grey wolf elite PSO balanced XGBoost for radar emitter individual identification based on measured signals[J]. Measurement, 2020, 159, 107777. doi: 10.1016/j.measurement.2020.107777
7	HARIKALA T , SATYA N R . PSO-optimized Pareto and Nash equilibrium gaming-based power allocation technique for multistatic radar network[J]. ETRI Journal, 2021, 43 (1): 17- 30. doi: 10.4218/etrij.2019-0351
8	SIVARANJANI R , ROOMI S M M , SENTHILARASI M . Speckle noise removal in SAR images using multi-objective PSO (MOPSO) algorithm[J]. Applied Soft Computing, 2019, 76, 671- 681. doi: 10.1016/j.asoc.2018.12.030
9	YIGIT E . A translational motion compensation technique for inverse synthetic aperture radar images using multi-objective particle swarm optimization algorithm[J]. Microwave and Optical Technology Letters, 2020, 62 (6): 2217- 2225. doi: 10.1002/mop.32314
10	ZHANG L , SHI C L , NIU J , et al. DOA estimation for HFSWR target based on PSO-ELM[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19, 3504205.
11	ALIZADEH M M , HOSSEINI S E . Pattern synthesize of a cylindrical conformal array antenna by PSO algorithm[J]. International Journal of RF and Microwave Computer-Aided Engineering, 2020, 30 (4): e22137.
12	韩江洪, 李正荣, 魏振春. 一种自适应粒子群优化算法及其仿真研究[J]. 系统仿真学报, 2006, 18 (10): 2969- 2971. doi: 10.3969/j.issn.1004-731X.2006.10.070
	HAN J H , LI Z R , WEI Z C . An adaptive particle swarm optimization algorithm and its simulation research[J]. Journal of System Simulation, 2006, 18 (10): 2969- 2971. doi: 10.3969/j.issn.1004-731X.2006.10.070
13	姜建国, 田旻, 王向前, 等. 采用扰动加速因子的自适应粒子群优化算法[J]. 西安电子科技大学学报, 2012, 39 (4): 74- 80. doi: 10.3969/j.issn.1001-2400.2012.04.014
	JIANG J G , TIAN M , WANG X Q , et al. Adaptive particle swarm optimization via disturbing acceleration coefficents[J]. Journal of Xidian University, 2012, 39 (4): 74- 80. doi: 10.3969/j.issn.1001-2400.2012.04.014
14	王存睿, 段晓东, 刘向东, 等. 改进的基本粒子群优化算法[J]. 计算机工程, 2004, 30 (21): 35- 37. doi: 10.3969/j.issn.1000-3428.2004.21.016
	WANG C R , DUAN X D , LIU X D , et al. A modified basic particle swarm optimization algorithm[J]. Computer Engineering, 2004, 30 (21): 35- 37. doi: 10.3969/j.issn.1000-3428.2004.21.016
15	PLUHACEK M , SENKERIK R , DAVENDRA D . Chaos particle swarm optimization with Eensemble of chaotic systems[J]. Swarm and Evolutionary Computation, 2015, 25, 29- 35. doi: 10.1016/j.swevo.2015.10.008
16	丛琳, 焦李成, 沙宇恒. 正交免疫克隆粒子群多目标优化算法[J]. 电子与信息学报, 2008, 30 (10): 2320- 2324.
	CONG L , JIAO L C , SHA Y H . Orthogonal immune clone particle swarm algorithm on multi-objective optimization[J]. Journal of Electronics & Information Technology, 2008, 30 (10): 2320- 2324.
17	鲁华祥, 尹世远, 龚国良, 等. 基于深度确定性策略梯度的粒子群算法[J]. 电子科技大学学报, 2021, 50 (2): 199- 206.
	LU H X , YIN S Y , GONG G L , et al. A particle swarm optimization algorithm based on deep deterministic policy gradient[J]. Journal of University of Electronic Science and Technology of China, 2021, 50 (2): 199- 206.
18	DING Z , HUANG Y H , YUAN H , et al. Introduction to reinforcement learning[M]. Singapore: Springer, 2020: 47- 123.
19	闫群民, 马瑞卿, 马永翔, 等. 一种自适应模拟退火粒子群优化算法[J]. 西安电子科技大学学报, 2021, 48 (4): 120- 127.
	YAN Q M , MA R Q , MA Y X , et al. Adaptive simulated annealing particle swarm optimization algorithm[J]. Journal of Xidian University, 2021, 48 (4): 120- 127.
20	GUILMEAU T, CHOUZENOUX E, ELVIRA V. Simulated annealing: a review and a new scheme[C]//Proc. of the IEEE Statistical Signal Processing Workshop, 2021: 101-105.
21	CAO H P , AN X M . A sparse Quasi-Newton method based on automatic differentiation for solving unconstrained optimization problems[J]. Symmetry, 2021, 13 (11): 2093- 2113. doi: 10.3390/sym13112093
22	CHAITANYA K , SOMAYAJULU D V L N , KRISHNA P R . Memory-based approaches for eliminating premature convergence in particle swarm optimization[J]. Applied Intelligence, 2021, 51 (7): 4575- 4608.
23	NAGRA A A , HAN F , LING Q H . An improved hybrid self-inertia weight adaptive particle swarm optimization algorithm with local search[J]. Engineering Optimization, 2019, 51 (7): 1115- 1132.
24	MOHAMMADI-IVATLOO B , RABIEE A , SOROUDI A , et al. Iteration PSO with time varying acceleration coefficients for solving non-convex economic dispatch problems[J]. International Journal of Electrical Power & Energy Systems, 2012, 42 (1): 508- 516.
25	焦巍, 刘光斌, 张艳红. 求解约束优化的模拟退火PSO算法[J]. 系统工程与电子技术, 2010, 32 (7): 1532- 1536.
	JIAO W , LIU G B , ZHANG Y H . Particle swarm optimization based on simulated annealing for solving constrained optimization problems[J]. Systems Engineering and Electronics, 2010, 32 (7): 1532- 1536.
26	PAN X Q , XUE L M , LU Y , et al. Hybrid particle swarm optimization with simulated annealing[J]. Multimedia Tools and Applications, 2019, 78 (21): 29921- 29936.
27	GARG H . A hybrid PSO-GA algorithm for constrained optimization problems[J]. Applied Mathematics and Computation, 2016, 274, 292- 305.
28	XU G P , CUI Q L , SHI X H , et al. Particle swarm optimization based on dimensional learning strategy[J]. Swarm and Evolutionary Computation, 2019, 45, 33- 51.
29	DURMUS A , KURBAN R , KAEAKOSE E . A comparison of swarm-based optimization algorithms in linear antenna array synthesis[J]. Journal of Computational Electronics, 2021, 20 (4): 1520- 1531.
30	包子阳, 余继周. 基于Matlab的遗传算法及其在稀布阵列天线中的应用[M]. 2版北京: 电子工业出版社, 2017.
	BAO Z Y , YU J Z . Genetic algorithm based on Matlab and its application in sparse array antenna[M]. 2ed Beijing: Publishing House of Electronics Industry, 2017.

函数名	表达式	x_max	v_max
Rosenbrock	$f_1=\sum\limits_{i=1}^{D-1}\left(100\left(x_i^2-x_{i+1}\right)^2+\left(x_i-1\right)^2\right)$	2.048	0.204 8
Levy	$\begin{aligned} f_2=& \sin ^2\left(\pi w_1\right)+\sum\limits_{i=1}^{D-1}\left(w_i-1\right)^2\left[1+10 \sin ^2\left(\pi w_i+1\right)\right]+\\ &\left(w_D-1\right)^2\left[1+\sin ^2\left(2 \pi w_D\right)\right]\left(w_i=1+\frac{x_i-1}{4}\right) \end{aligned}$	10	1
Schwefel	$f_3=418.9829 D-\sum\limits_{i=1}^D x_i \sin \left(\sqrt{\mid x_i}\right)$	500	50

函数	维度	Criterion	PSO	ASAPAO	AHAPSO
f₁	D=10	avg	2.347 7	5.06E-2	2.34E-22
	D=10	min	3.907 2E-11	2.109 7E-11	1.499 8E-31
	D=30	avg	11.414 6	2.055 3	0.064 3
	D=30	min	0.029 1	0.009 1	4.499 3E-31
f₂	D=10	avg	0.253 9	4.8E-3	7.15E-32
	D=10	min	1.32E-7	5.01E-8	0
	D=30	avg	33.85	14.84	0.047 3
	D=30	min	24.17	1.563 3	0
f₃	D=10	avg	1.25E03	1.07E03	1.03E02
	D=10	min	2.287 3E-4	1.42E-4	1.27E-4
	D=30	avg	6.72E03	4.36E03	1.95E03
	D=30	min	2.91E03	1.22E03	3.81E-4

[1]	Yue LIU, Xiaoqing ZHONG, Jinyu FU, Xiang LI. Stereo matching algorithm based on hybrid optimization method [J]. Systems Engineering and Electronics, 2020, 42(12): 2692-2699.
[2]	Jiadong YI, Jie YANG. Radar signal recognition based on IFOA-SA-BP neural network [J]. Systems Engineering and Electronics, 2020, 42(12): 2735-2741.
[3]	Tao XU, Zhishuai WU, Min LU, Zonglei LYU, Zhonghu LI. Optimization model of hub-and-spoke network for congestion problem [J]. Systems Engineering and Electronics, 2020, 42(11): 2553-2559.
[4]	LIU Jianye, WANG Hua, ZHOU Wanmeng. LEO constellation sensor resources scheduling algorithm based on Genetic and Simulated annealing [J]. Systems Engineering and Electronics, 2018, 40(11): 2476-.
[5]	WANG Minle, FAN Yangtao. Intelligent solving algorithm for effectsbased firepower allocation model of conventional missiles [J]. Systems Engineering and Electronics, 2017, 39(11): 2509-2514.
[6]	LI An-da, HE Zhen, HE Shu-guang. Critical to quality characteristics identification for complex products using GSA [J]. Systems Engineering and Electronics, 2015, 37(9): 2073-2079.
[7]	YAO Ying-biao, WANG Xuan. Modified hybrid genetic algorithm for parallel task scheduling of multiprocessors [J]. Systems Engineering and Electronics, 2015, 37(8): 1928-1935.
[8]	YAN Qing-qing, SHEN Huai-rong, SHAO Qiong-ling. Space object ground-based surveillance scheduling based on genetic-simulated annealing algorithm [J]. Systems Engineering and Electronics, 2015, 37(12): 2764-2771.
[9]	HE Xiao-liang, BI Yi-ming. Modeling and optimization of formation air-to-ground attack fire distribution based on simulated annealing genetic algorithm [J]. Systems Engineering and Electronics, 2014, 36(5): 900-904.
[10]	ZHAO Yong-bo, LI Hui, QIN Chun-miao. DFCW-LFM waveform design based on GASA algorithm [J]. Systems Engineering and Electronics, 2014, 36(11): 2186-2191.
[11]	WANG Hui-ying, WANG Wen-bin. Hybrid bee colony algorithm with modified search strategy [J]. Systems Engineering and Electronics, 2014, 36(10): 2094-2101.
[12]	GONG Shu-feng，BEN De，PAN Ming-hai. Discrete frequency code waveform design for OAR based on HGSAA [J]. Systems Engineering and Electronics, 2013, 35(9): 1854-1860.
[13]	ZHANG Wei， HE Zi-shu， LI Jun. Optimization design of MIMO radar sparse array [J]. Journal of Systems Engineering and Electronics, 2013, 35(2): 299-303.
[14]	ZHANG Shu,LI Liang. Optimal pattern synthesis with nulls for circular array based on directional elements [J]. Journal of Systems Engineering and Electronics, 2012, 34(12): 2418-2422.
[15]	WU Hong,WANG Wei-ping,YANG Feng. Structure learning method of Bayesian network with prior information [J]. Journal of Systems Engineering and Electronics, 2012, 34(12): 2585-2591.

Adaptive hybrid annealing particle swarm optimization algorithm

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 30

Related Articles 15

Recommended Articles

Metrics

Comments