Membrane Computing Multi Particle Swarm Optimization(MC-MPSO) Algorithm

doi:10.3901/JME.2019.12.222

Abstract

Abstract: Membrane computing multi particle swarm optimization(MC-MPSO) algorithm is proposed to overcome of particle swarm opmtimization(PSO) algorithm the defections of easy getting trapped in a local optimum, slow convergent speed and low accuracy in the later evolution process. In MC-MPSO algorithm, original PSO, standard PSO, median-oriented PSO(MPSO), extended PSO(EPSO), multi force PSO(MFPSO), two-stage force PSO(TFPSO) with different advantages of particle swarm algorithm are put into six membranes respectively. The communication among membranes and update mechanisms of particles are proposed in MC-MPSO. First of all, elementary membrane grow up according to their own searching mechanism with the advantages of each PSO algorithm. Secondly, six algorithms in the membranes exchange optimally with better membrane, and the surface membrane gradually swallow the membranes of poor searching ability. Then the membranes which can solve the problems properly grow up and the best membrane export through surface membrane. The MC-MPSO algorithm integrates the advantages of the six particle swarm optimization algorithms, and has the ability to adapt to different types of optimization problems. By comparing with the test of six algorithms in the membranes, the comparison of genetic algorithm, fish swarm algorithm and other particle swarm optimization algorithms based on membrane computing, the results show that the MC-MPSO algorithm has better search capability of optimal solution and wide applicability. Finally, the MC-MPSO algorithm is applied in the reliability optimization of series and bridge systems. The effectiveness of the proposed algorithm is verified.

Key words: MC-MPSO algorithm, membrane computing, PSO algorithm, reliability optimization

CLC Number:

TP18
TB114

CHEN Dongning, WANG Yueying, YAO Chengyu, LIU Yidan, Lü Shijun. Membrane Computing Multi Particle Swarm Optimization(MC-MPSO) Algorithm[J]. Journal of Mechanical Engineering, 2019, 55(12): 222-232.

References

[1] KENNEDY J, EBERHART R C. Particle swarm optimization[C]//IEEE International Conference on Neural Networks, November 27-December 1, 1995, Perth, WA, Australia, IEEE, 1995:1942-1948.
[2] OUYANG H, GAO L, KONG X, et al. Hybrid harmony search particle swarm optimization with global dimension selection[J]. Information Sciences, 2016, 6(346-347):318-337.
[3] 谢延敏,孙新强,田银,等.基于改进粒子群算法和小波神经网络的高强钢扭曲回弹工艺参数优化[J].机械工程学报,2016,52(19):162-167. XIE Yanmin, SUN Xinqiang, TIAN Yin, et al. Optimization of parameters in twist springback process for high-strength sheets based on improved particle swarm optimization algorithm and wavelet neural network[J]. Journal of Mechanical Engineering, 2016, 52(19):162-167.
[4] 姚成玉,王斌,陈东宁,等.混合粒子交互微粒群算法[J].机械工程学报,2015,51(6):198-207. YAO Chengyu, WANG bin, CHEN Dongning, et al. Hybrid-particle interaction particle swarm optimization algorithm[J]. Journal of Mechanical Engineering, 2015, 51(6):198-207.
[5] TANG M, XIN Y. Energy efficient power allocation in cognitive radio network using coevolution chaotic particle swarm optimization[J]. Computer Networks, 2016, 100:1-11.
[6] 王小巧,刘明周,葛茂根,等.基于混合粒子群算法的复杂机械产品装配质量控制阈优化方法[J].机械工程学报,2016,52(1):130-138. WANG Xiaoqiao, LIU Mingzhou, GE Maogen, et al. Online control threshold optimization for complex mechanical products assembly process based on hybrid genetic particle swarm optimization[J]. Journal of Mechanical Engineering, 2016, 52(1):130-138.
[7] CHEN Dongning, ZHANG Ruixing, YAO Chengyu, et al. Dynamic topology multi force particle swarm optimization algorithm and its application[J]. Chinese Journal of Mechanical Engineering, 2016, 29(1):124-135.
[8] 周蓉,沈维蕾,刘明周,等. 带时间窗装卸一体化车辆路径问题的混合离散粒子群优化算法[J]. 中国机械工程, 2016, 27(4):494-502. ZHOU Rong, SHEN Weilei, LIU Mingzhou, et al. A hybrid discrete particle swarm optimization algorithm for vehicle routing problem with time windows and simultaneous pickup and deliver[J]. China Mechanical Engineering, 2016, 27(4):494-502.
[9] NGO T T, SADOLLAH A, KIM J K. A cooperative particle swarm optimizer with stochastic movements for computationally expensive numerical optimization problems[J]. Journal of Computational Science, 2016, 13:68-82.
[10] EBERHART R C, SHI Y. Particle swarm optimization:developments, applications and resources[C]//IEEE Congress on Evolutionary Computation, May 27-30, 2001, Seoul, Korea, IEEE, 2001:81-86.
[11] 安鹏.基于混沌理论和自适应惯性权重的PSO算法优化[J].吉林大学学报(理学版),2015,53(6):1224-1228. AN Peng. Optimization of PSO algorithm based on chaotic theory[J]. Journal of Jilin University(Science Edition), 2015, 53(6):1224-1228.
[12] BEHESHTI Z, SHAMSUDDIN S M, HASAN S. MPSO:Median-oriented particle swarm optimization[J]. Applied Mathematics and Computation, 2013, 219(11):5817-5836.
[13] 莫思敏,曾建潮,谢丽萍.扩展的微粒群算法[J].控制理论与应用,2012,29(6):811-816. MO Simin, ZENG Jianchao, XIE Liping. Extended particle-swarm optimization algorithm[J], Control Theory & Applications, 2012, 29(6):811-816.
[14] 陈东宁,张瑞星,姚成玉,等.求解液压阀块加工车间调度的多作用力微粒群算法[J].中国机械工程,2015,26(3):369-378. CHEN Dongning, ZHANG Ruixing, YAO Chengyu, et al. A mfpso algorithm for solving hydraulic manifold processing shop scheduling[J]. China Mechanical Engineering, 2015, 26(3):369-378.
[15] YAO Chengyu, TAN Xueyun, CHEN Dongning, et al. Two-stage force particle swarm optimization algorithm and application in hydraulic system reliability optimization[C]//International Conference on Fluid Power and Mechatronics, August 5-7, 2015, Harbin, IEEE, 2015:1291-1296.
[16] PǍUN G, ROZENBERG G, SALOMAA A. The oxford handbook of membrane computing[M]. New York:Oxford University Press, 2010.
[17] SINGH G, DEEP K, NAGAR A K. Cell-like P-systems based on rules of particle swarm optimization[J]. Applied Mathematics and Computation, 2014, 246:546-560.
[18] ZHANG G, GHEORGHE M, PAN L, et al. Evolutionary membrane computing:a comprehensive survey and new results[J]. Information Sciences, 2014, 279:528-551.
[19] 黄亮.膜计算优化方法研究[D].杭州:浙江大学,2007. HUANG Liang. Research on membrane computing optimization methods[D]. Hangzhou:Zhejiang University, 2007.
[20] 欧柱.细胞型膜计算模型遗传优化设计研究[D].成都:西南交通大学,2013. OU Zhu. Genetic optimization design of cell-like membrane computing models[D]. Chengdu:Southwest Jiaotong University, 2013.
[21] 廖孝勇,李尚键,孙棣华,等.一种基于膜计算的改进人工鱼群算法[J].小型微型算机系统,2014,35(5):1142-1146. LIAO Xiaoyong, LI Shangjian, SUN Dihua, et al. An improved artificial fish swarm algorithm based on membrane computing[J]. Journal of Chinese Computer Systems. 2014, 35(5):1142-1146.
[22] 杜强,向来生,刘希玉.基于P系统的粒子群优化算法[J].计算机应用研究,2013,30(8):2269-2272. DU Qiang, XIANG Laisheng, LIU Xiyu. P system based particle swarm optimization algorithm[J]. Application Research of Computers, 2013, 30(8):2269-2272.
[23] ZHOU Fen, ZHANG Gexiang, RONG Haina, et al. A particle swarm optimization based on P systems[C]//International Conference on Natural Computation, August 10-12, 2010, Yantai, IEEE, 2010:3003-3007.
[24] 史峰,王辉,郁磊,等.MATLAB智能算法30个案例分析[M].北京:北京航空航天大学出版社,2011. SHI Feng, WANG Hui, YU Lei, et al. 30 cases of MATLAB intelligent algorithm[M]. Beijing:Beihang University Press, 2011.
[25] 周芬.粒子群膜算法及其应用研究[D].成都:西南交通大学,2011. ZHOU Fen. Particle swarm optimization membrane algorithm and its application research[D]. Chengdu:Southwest Jiaotong University, 2011.
[26] 陈东宁,姚成玉.基于T-S故障树和混合μPSO算法的可靠性优化方法[J].中国机械工程,2013,24(18):2415-2420. CHEN Dongning, YAO Chengyu. Reliability optimization method based on T-S fault tree and hybrid μPSO algorithm[J]. China Mechanical Engineering, 2013, 24(18):2415-2420.