67 Downloads 432 Views
Author(s)
Mengshan LI, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China, 341000
Huaijin ZHANG, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China, 341000
Bingsheng CHEN, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China, 341000
Lixin GUAN, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China, 341000
Yan WU, College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, China, 341000
Abstract
In view of the recently proposed acceleration particle swarm optimization with strong global search capability, a chaos enhanced particles swarm optimization algorithm based chaos theory is proposed. Hybrid chaotic sequence is introduced to adjust the global learning factor, and the algorithm can further increase the global search ability. The performance of the algorithm is verified by testing four typical multiobjective optimization functions, and compared with the classic noninferiority classification multiobjective genetic algorithm, multiobjective particle swarm optimization algorithm and acceleration particle swarm optimization algorithm. The result shows that the Hybrid chaotic acceleration particle swarm optimization algorithm has faster convergence speed and stronger ability to jump out of local optimization, and the performance is superior.
Keywords
Particle Swarm Optimization; Hybrid chaotic; Acceleration algorithm.
Cite this paper
Mengshan LI,
Huaijin ZHANG,
Bingsheng CHEN,
Lixin GUAN,
Yan WU,
Hybrid chaotic enhanced acceleration particle swarm optimization algorithm, SCIREA Journal of Computer. Vol.
3
, No.
1
,
2018
, pp.
16

30
.
References
[ 1 ]  Zhu Q.L., Lin, Q.Z., Chen, W.N., Wong, K.C., Coello, C.A.C., Li, J.Q., Chen, J.Y., Zhang, J. An External ArchiveGuided Multiobjective Particle Swarm Optimization Algorithm. IEEE Transactions on Cybernetics, 2017, 47(9): 27942808. 
[ 2 ]  Neri F., Mininno, E., Lacca, G. Compact Particle Swarm Optimization. Information Sciences, 2013, 239: 96121. 
[ 3 ]  Zitzler E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G. Performance assessment of multiobjective optimizers: An analysis and review. Ieee Transactions On Evolutionary Computation, 2003, 7(2): 117132. 
[ 4 ]  Shirazian S., Alibabaei, M. Using neural networks coupled with particle swarm optimization technique for mathematical modeling of air gap membrane distillation (AGMD) systems for desalination process. Neural Computing & Applications, 2017, 28(8): 20992104. 
[ 5 ]  Tsekouras G.E., Tsimikas, J. On training RBF neural networks using inputoutput fuzzy clustering and particle swarm optimization. Fuzzy Sets And Systems, 2013, 221: 6589. 
[ 6 ]  Yan J., He, W.X., Jiang, X.L., Zhang, Z.L. A novel phase performance evaluation method for particle swarm optimization algorithms using velocitybased state estimation. Applied Soft Computing, 2017, 57: 517525. 
[ 7 ]  Javidrad F., Nazari, M. A new hybrid particle swarm and simulated annealing stochastic optimization method. Applied Soft Computing, 2017, 60: 634654. 
[ 8 ]  Kiran M.S. Particle swarm optimization with a new update mechanism. Applied Soft Computing, 2017, 60: 670678. 
[ 9 ]  Han H.G., Lu, W., Qiao, J.F. An Adaptive Multiobjective Particle Swarm Optimization Based on Multiple Adaptive Methods. IEEE Transactions on Cybernetics, 2017, 47(9): 27542767. 
[ 10 ]  Gou J., Lei, Y.X., Guo, W.P., Wang, C., Cai, Y.Q., Luo, W. A novel improved particle swarm optimization algorithm based on individual difference evolution. Applied Soft Computing, 2017, 57: 468481. 
[ 11 ]  Liu Q.X., van Wyk, B.J., Du, S.Z., Sun, Y.X. Dynamic Small World Network Topology for Particle Swarm Optimization. International Journal Of Pattern Recognition And Artificial Intelligence, 2016, 30(9). 
[ 12 ]  Gong Y.J., Li, J.J., Zhou, Y.C., Li, Y., Chung, H.S.H., Shi, Y.H., Zhang, J. Genetic Learning Particle Swarm Optimization. IEEE Transactions on Cybernetics, 2016, 46(10): 22772290. 
[ 13 ]  Akay B. A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Applied Soft Computing, 2013, 13(6): 30663091. 
[ 14 ]  Khare A., Rangnekar, S. A review of particle swarm optimization and its applications in Solar Photovoltaic system. Applied Soft Computing, 2013, 13(5): 29973006. 
[ 15 ]  Valdez F., Melin, P., Castillo, O. An improved evolutionary method with fuzzy logic for combining Particle Swarm Optimization and Genetic Algorithms. Applied Soft Computing, 2011, 11(2): 26252632. 
[ 16 ]  Marinakis Y., Marinaki, M. Particle swarm optimization with expanding neighborhood topology for the permutation flowshop scheduling problem. Soft Computing, 2013, 17(7): 11591173. 
[ 17 ]  Zhang Z.B., Jiang, Y.Z., Zhang, S.H., Geng, S.M., Wang, H., Sang, G.Q. An adaptive particle swarm optimization algorithm for reservoir operation optimization. Applied Soft Computing, 2014, 18: 167177. 
[ 18 ]  Sinha A.K., Zhang, W.J., Tiwari, M.K. Coevolutionary immunoparticle swarm optimization with penetrated hypermutation for distributed inventory replenishment. Engineering Applications Of Artificial Intelligence, 2012, 25(8): 16281643. 
[ 19 ]  Cheng M.Y., Huang, K.Y., Chen, H.M. Kmeans particle swarm optimization with embedded chaotic search for solving multidimensional problems. Applied Mathematics And Computation, 2012, 219(6): 30913099. 
[ 20 ]  Li M.W., Kang, H.G., Zhou, P.F., Hong, W.C. Hybrid optimization algorithm based on chaos, cloud and particle swarm optimization algorithm. Journal of Systems Engineering and Electronics, 2013, 24(2): 324334. 
[ 21 ]  Qasem S.N., Shamsuddin, S.M., Hashim, S.Z.M., Darus, M., AlShammari, E. Memetic multiobjective particle swarm optimizationbased radial basis function network for classification problems. Information Sciences, 2013, 239: 165190. 
[ 22 ]  Zheng Y.J., Chen, S.Y. Cooperative particle swarm optimization for multiobjective transportation planning. Applied Intelligence, 2013, 39(1): 202216. 
[ 23 ]  Wang L., Yang, B., Chen, Y.H. Improving particle swarm optimization using multilayer searching strategy. Information Sciences, 2014, 274: 7094. 
[ 24 ]  Tsekouras G.E. A simple and effective algorithm for implementing particle swarm optimization in RBF network's design using inputoutput fuzzy clustering. Neurocomputing, 2013, 108: 3644. 
[ 25 ]  Mac T.T., Copot, C., Tran, D.T., De Keyser, R. A hierarchical global path planning approach for mobile robots based on multiobjective particle swarm optimization. Applied Soft Computing, 2017, 59: 6876. 
[ 26 ]  Jiang F., Xia, H.Y., Tran, Q.A., Ha, Q.M., Tran, N.Q., Hu, J.K. A new binary hybrid particle swarm optimization with wavelet mutation. Knowledgebased Systems, 2017, 130: 90101. 
[ 27 ]  Li P.C., Xiao, H. An improved quantumbehaved particle swarm optimization algorithm. Applied Intelligence, 2014, 40(3): 479496. 
[ 28 ]  Wang Y., Li, Y.Y., Chen, Z.H., Xue, Y. Cooperative particle swarm optimization using MapReduce. Soft Computing, 2017, 21(22): 65936603. 
[ 29 ]  Meng M., Rizvi, M.J., Le, H.R., Grove, S.M. Multiscale modelling of moisture diffusion coupled with stress distribution in CFRP laminated composites. Composite Structures, 2016, 138: 295304. 
[ 30 ]  Zhao L.P., SHU, Q.L., WU, Y., LI, M.S. Chaosenhanced Accelerated Particle Swarm Optimization Algorithm. Application Research of computers, 2014, 31(08): 23072310. 
[ 31 ]  Kennedy J., Eberhart, R., Particle swarm optimization, in: 1995 IEEE International Conference on Neural Networks Proceedings, Proceedings of ICNN'95  International Conference on Neural Networks, IEEE Australia Council, Perth, 1995, pp. 19421948. 
[ 32 ]  Kennedy J., Eberhart, R., Particle swarm optimization, in: Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6), IEEE, Perth, Aust, 1995, pp. 19421948. 
[ 33 ]  Eberhart R., Kennedy, J., New optimizer using particle swarm theory, in: Proceedings of the 1995 6th International Symposium on Micro Machine and Human Science, October 4, 1995  October 6, 1995, IEEE, Nagoya, Jpn, 1995, pp. 3943. 
[ 34 ]  Schaffer J.D., Multiple Objective Optimization with Vector Evaluated Genetic Algorithms, in: International Conference on Genetic Algorithms  ICGA 85, Lawrence Erlbaum Associates, Netherlands, 1985, pp. 93100. 
[ 35 ]  Zitzler E., Thiele, L. Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach. Ieee Transactions On Evolutionary Computation, 1999, 3(4): 257271. 
[ 36 ]  Deb K. Multiobjective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evolutionary Computation, 1999, 7(3): 205230. 
[ 37 ]  Zitzler E., Deb, K., Thiele, L. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 2000, 8(2): 173195. 
[ 38 ]  Deb K., Pratap, A., Agarwal, S., Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGAII. Ieee Transactions On Evolutionary Computation, 2002, 6(2): 182197. 