Chaos Adaptive Improved Particle Swarm Optimization Algorithm and its application in Multi-objective Optimization

Volume 3, Issue 1, February 2018     |     PP. 1-15      |     PDF (366 K)    |     Pub. Date: January 2, 2018
DOI:    378 Downloads     7425 Views  

Author(s)

CHEN Bingsheng, Gannan Normal University, Ganzhou, Jiangxi 341000, China
LIU Liang, Gannan Normal University, Ganzhou, Jiangxi 341000, China
SU Keming, Gannan Normal University, Ganzhou, Jiangxi 341000, China
ZHANG Huaijin, Gannan Normal University, Ganzhou, Jiangxi 341000, China
LI Mengshan, Gannan Normal University, Ganzhou, Jiangxi 341000, China

Abstract
To overcome the problem of premature convergence on particle swarm optimization (PSO), this paper proposes an improved particle swarm optimization method (IPSO) that based on self-adaptive regulation strategy and chaos theory. For a given the effective balance of particles’ searching and development ability, self-adaptive regulation strategy is employed to optimize the inertia weight. To improve efficiency and quality of search, learning factor is optimized by generating Chaotic Sequences by Chaos Theory. The proposed improved methods achieve better convergence performance and increases searching speed. Simulation results of some typical optimization problems and comparisons with typical multi-objective optimization algorithms show that IPSO has an ability of fast convergence speed, and the diversity of non-dominated and the convergence are ideal. The algorithm meets requirements of multi-objective optimization Problem.

Keywords
Particle Swarm Optimization, multi-objective optimization, Chaos Theory, self-adaptive regulation strategy

Cite this paper
CHEN Bingsheng, LIU Liang, SU Keming, ZHANG Huaijin, LI Mengshan, Chaos Adaptive Improved Particle Swarm Optimization Algorithm and its application in Multi-objective Optimization , SCIREA Journal of Computer. Volume 3, Issue 1, February 2018 | PP. 1-15.

References

[ 1 ] ZITZLER, E., THIELE, L. Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach[J]. Ieee Transactions On Evolutionary Computation, 1999, 3(4): 257-271.
[ 2 ] ZHU, Q. L., LIN, Q. Z., CHEN, W. N., ET AL. An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm[J]. IEEE Transactions on Cybernetics, 2017, 47(9): 2794-2808.
[ 3 ] WANG, L., YANG, B., ORCHARD, J. Particle swarm optimization using dynamic tournament topology[J]. Applied Soft Computing, 2016, 48: 584-596.
[ 4 ] LIU, J. H., MEI, Y., LI, X. D. An Analysis of the Inertia Weight Parameter for Binary Particle Swarm Optimization[J]. Ieee Transactions On Evolutionary Computation, 2016, 20(5): 666-681.
[ 5 ] ZHENG, L. M., WANG, Q., ZHANG, S. X., ET AL. Population recombination strategies for multi-objective particle swarm optimization[J]. Soft Computing, 2017, 21(16): 4693-4705.
[ 6 ] KAMPOLIS, I. C., GIANNAKOGLOU, K. C. A multilevel approach to Single- and multiobjective aerodynamic optimization[J]. Computer Methods in Applied Mechanics and Engineering, 2008, 197(33-40): 2963-2975.
[ 7 ] YAN, J., HE, W. X., JIANG, X. L., ET AL. A novel phase performance evaluation method for particle swarm optimization algorithms using velocity-based state estimation[J]. Applied Soft Computing, 2017, 57: 517-525.
[ 8 ] QIN, Q. D., CHENG, S., ZHANG, Q. Y., ET AL. Particle Swarm Optimization With Interswarm Interactive Learning Strategy[J]. IEEE Transactions on Cybernetics, 2016, 46(10): 2238-2251.
[ 9 ] MOHIUDDIN, M. A., KHAN, S. A., ENGELBRECHT, A. P. Fuzzy particle swarm optimization algorithms for the open shortest path first weight setting problem[J]. Applied Intelligence, 2016, 45(3): 598-621.
[ 10 ] LI, L. S., LAI, K. K. A fuzzy approach to the multiobjective transportation problem[J]. Computers & Operations Research, 2000, 27(1): 43-57.
[ 11 ] GUNZBURGER, M. D., LEE, J. A domain decomposition method for optimization problems for partial differential equations[J]. Computers & Mathematics with Applications, 2000, 40(2-3): 177-192.
[ 12 ] ZITZLER, E., DEB, K., THIELE, L. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results[J]. Evolutionary Computation, 2000, 8(2): 173-195.
[ 13 ] DEB, KALYANMOY. Multi-objective genetic algorithms: Problem difficulties and construction of test problems[J]. Evolutionary Computation, 1999, 7(3): 205-230.
[ 14 ] LAUMANNS, M., THIELE, L., DEB, K., ET AL. Combining convergence and diversity in evolutionary multiobjective optimization[J]. Evolutionary Computation, 2002, 10(3): 263-282.
[ 15 ] JIANG, F., XIA, H. Y., TRAN, Q. A., ET AL. A new binary hybrid particle swarm optimization with wavelet mutation[J]. Knowledge-based Systems, 2017, 130: 90-101.
[ 16 ] JAVIDRAD, F., NAZARI, M. A new hybrid particle swarm and simulated annealing stochastic optimization method[J]. Applied Soft Computing, 2017, 60: 634-654.
[ 17 ] HAN, H. G., LU, W., QIAO, J. F. An Adaptive Multiobjective Particle Swarm Optimization Based on Multiple Adaptive Methods[J]. IEEE Transactions on Cybernetics, 2017, 47(9): 2754-2767.
[ 18 ] FONSECA, CARLOS M, FLEMING, PETER J. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization [C]. // Proceedings of the Fifth International Conference on Genetic Algorithms. San Mateo, California, 1993.416-423.
[ 19 ] REY HORN, JE, NAFPLIOTIS, NICHOLAS, GOLDBERG, DAVID E. Multiobjective optimization using the niched pareto genetic algorithm[J]. IlliGAL report, 1993, (93005): 61801-62296.
[ 20 ] SRINIVAS, NIDAMARTHI, DEB, KALYANMOY. Muiltiobjective optimization using nondominated sorting in genetic algorithms[J]. Evolutionary Computation, 1994, 2(3): 221-248.
[ 21 ] Zitzler, E., Thiele, L. Multiobjective optimization using evolutionary algorithms - A comparative case study. In: Eiben AE，Back T，Schoenauer M,Schwefel HP, (eds.). Parallel Problem Solving from Nature - Ppsn V1998, p. 292-301.
[ 22 ] DEB, K., PRATAP, A., AGARWAL, S., ET AL. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. Ieee Transactions On Evolutionary Computation, 2002, 6(2): 182-197.
[ 23 ] YUHUI, SHI, EBERHART, R. C. Fuzzy adaptive particle swarm optimization[J]. Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), 2001: 101-106 vol. 101.
[ 24 ] ZHOU, SONG-HUA, OUYANG, CHUN-JUAN, LIU, CHANG-XIN, ET AL. Adaptive fuzzy particle swarm optimization algorithm[J]. Computer Engineering and Applications, 2010, 46(33): 46-48.
[ 25 ] HE, S., WU, Q. H., WEN, J. Y., ET AL. A particle swarm optimizer with passive congregation[J]. Biosystems, 2004, 78(1-3): 135-147.
[ 26 ] TAN, K. C., LEE, T. H., KHOR, E. F. Evolutionary algorithms for multi-objective optimization: Performance assessments and comparisons[J]. Artificial Intelligence Review, 2002, 17(4): 253-290.
[ 27 ] ZITZLER, E., THIELE, L., LAUMANNS, M., ET AL. Performance assessment of multiobjective optimizers: An analysis and review[J]. Ieee Transactions On Evolutionary Computation, 2003, 7(2): 117-132.
[ 28 ] 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[J]. Neural Computing & Applications, 2017, 28(8): 2099-2104.
[ 29 ] MAC, T. T., COPOT, C., TRAN, D. T., ET AL. A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization[J]. Applied Soft Computing, 2017, 59: 68-76.
[ 30 ] KIRAN, M. S. Particle swarm optimization with a new update mechanism[J]. Applied Soft Computing, 2017, 60: 670-678.
[ 31 ] GONG, Y. J., LI, J. J., ZHOU, Y. C., ET AL. Genetic Learning Particle Swarm Optimization[J]. IEEE Transactions on Cybernetics, 2016, 46(10): 2277-2290.
[ 32 ] KENNEDY, J., EBERHART, R. Particle swarm optimization[J]. 1995 IEEE International Conference on Neural Networks Proceedings (Cat. No.95CH35828), 1995: 1942-1948 vol.1944.
[ 33 ] KENNEDY, J., EBERHART, R. Particle swarm optimization [C]. // 1995 IEEE International Conference on Neural Networks Proceedings, Proceedings of ICNN'95 - International Conference on Neural Networks. Perth: IEEE Australia Council, 1995.1942-1948.
[ 34 ] SCHAFFER, J DAVID. Multiple objective optimization with vector evaluated genetic algorithms [C]. // Proceedings of the 1st international Conference on Genetic Algorithms. L. Erlbaum Associates Inc., 1985.93-100.