A novel particle swarm optimizer hybridized with extremal optimization

Applied Soft Computing Journal

Volumn 10, Issue 2, 2010, Pages 367-373

  Chen, Min Rong ^a   Li, Xia ^a   Zhang, Xi ^a   Lu, Yong Zai ^b  

^a SHENZHEN UNIVERSITY   (China)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Extremal optimization; Meta heuristics; Multimodal functions; Numerical optimization; Particle swarm optimization

Indexed keywords

EXTREMAL OPTIMIZATION; META HEURISTICS; MULTI MODAL FUNCTION; NUMERICAL OPTIMIZATION; NUMERICAL OPTIMIZATIONS;

CONVERGENCE OF NUMERICAL METHODS; GROUND STATE; HEURISTIC ALGORITHMS; HEURISTIC METHODS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 70649097014     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2009.08.014     Document Type: Article

References (41)

1. Kennedy J., and Eberhart R.C. Particle swarm optimization. Proceedings of the 1995 IEEE International Conference on Neural Networks. IEEE Service Center, Piscat away (1995) 1942-1948
2. Eberhart R.C., and Kennedy J. A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micromachine and Human Science. Nagoya, Japan (1995) 39-43
3. Angeline P.J. Using selection to improve particle swarm optimization. Proceedings of the IEEE Congress on Evolutionary Computation. Anchorage, Alaska, USA (1998) 84-89
4. Løvbjerg M., Rasmussen T.K., and Krink T. Hybrid particle swarm optimiser with breeding and subpopulations. Proceedings of the Genetic and Evolutionary Computation Conference (2001), Morgan Kaufmann Publishers, San Francisco 469-476
5. Parsopoulos K.E., Plagianakos V.P., Magoulas G.D., and Vrahatis M.N. Stretching technique for obtaining global minimizers through particle swarm optimization. Proceedings of the Workshop on Particle Swarm Optimization. Indianapolis, IN (2001) 22-29
6. Parsopoulos K.E., and Vrahatis M.N. Initializing the Particle Swarm Optimizer Using the Nonlinear Simplex Method. Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation (2002), WSEAS Press pp. 216-221
7. Higashi N., and Iba H. Particle swarm optimization with gaussian mutation. Proceedings of the IEEE Swarm Intelligence Symposium 2003. Indianapolis, Indiana, USA (2003) 72-79
8. Shi X.H., Lu Y.H., Zhou C.G., Lee H.P., Lin W.Z., and Liang Y.C. Hybrid evolutionary algorithms based on PSO and GA. Proceedings of IEEE Congress on Evolutionary Computation 2003. Canbella, Australia (2003) 2393-2399
9. Fan S.K.S., and Zahara E. A hybrid simplex search and particle swarm optimization for unconstrained optimisation. European Journal of Operational Research 181 (2007) 527-548
10. Jin Y., Joshua K., Lu H., Liang Y., and Douglas B.K. The landscape adaptive particle swarm optimizer. Applied Soft Computing 8 (2008) 295-304
11. Liu H., Abraham A., and Clerc M. Chaotic dynamic characteristics in swarm intelligence. Applied Soft Computing 7 (2007) 1019-1026
12. Chen D.B., and Zhao C.X. Particle swarm optimization with adaptive population size and its application. Applied Soft Computing (2008) doi:10.1016/j.asoc.2008.03.001
13. Senthil Arumugam M., Rao M.V.C., and Tan A.W.C. A novel and effective particle swarm optimization like algorithm with extrapolation technique. Applied Soft Computing (2008) doi:10.1016/j.asoc.2008.04.016
14. Kao Y.T., and Zahara E. A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Applied Soft Computing 8 (2008) 849-857
15. Senthil Arumugam M., and Rao M.V.C. On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems. Applied Soft Computing 8 (2008) 324-336
16. Jiang Y., Hu T., Huang C., and Wu X. An improved particle swarm optimization algorithm. Applied Mathematics and Computation 193 (2007) 231-239
17. Brits R., Engelbrecht A.P., and van den Bergh F. Locating multiple optima using particle swarm optimisation. Applied Mathematics and Computation 189 (2007) 1859-1883
18. Shelokar P.S., Patrick Siarry V.K., Jayaraman B.D., and Kulkarni. Particle swarm and ant colony algorithms hybridized for improved continuous optimisation. Applied Mathematics and Computation 188 (2007) 129-142
19. Xiang T., Wong K., and Liao X. A novel particle swarm optimizer with time-delay. Applied Mathematics and Computation 186 (2007) 789-793
20. Xiang T., Liao X., and Wong K. An improved particle swarm optimization algorithm combined with piecewise linear chaotic map. Applied Mathematics and Computation 190 (2007) 1637-1645
21. Niu B., Zhu Y., He X., and Wu H. MCPSO: a multi-swarm cooperative particle swarm optimiser. Applied Mathematics and Computation 185 (2007) 1050-1062
22. Yang X., Yuan J., Yuan J., and Mao H. A modified particle swarm optimizer with dynamic adaptation. Applied Mathematics and Computation 189 (2007) 1205-1213
23. Boettcher S., and Percus A.G. Extremal optimization: methods derived from co-evolution. Proceedings of the Genetic and Evolutionary Computation Conference (1999) 825-832
24. Boettcher S., and Percus A.G. Nature's way of optimising. Artificial Intelligence 119 (2000) 275-286
25. Bak P., and Sneppen K. Punctuated equilibrium and criticality in a simple model of evolution. Physical Review Letters 71 (1993) 4083-4086
26. Bak P., Tang C., and Wiesenfeld K. Self-organized criticality. Physical Review Letters 59 (1987) 381-384
27. Boettcher S., and Percus A.G. Optimizing Glasses with Extremal Dynamics. Computer Simulation Studies in Condensed-Matter Physics XVI. Part II (2006), Springer, Berlin, Heidelberg pp.74-79
28. Boettcher S., and Percus A.G. Extremal optimization at the phase transition of the 3-coloring problem. Physical Review E 69 (2004) 066703
29. Lu Y.-Z., Chen M.-R., and Chen Y.-W. Studies on extremal optimization and its applications in solving real world optimization problems. Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence (FOCI 2007). Hawaii, USA (2007) 162-168
30. Chen Y.-W., Lu Y.-Z., and Yang G. Hybrid evolutionary algorithm with marriage of genetic algorithm and extremal optimization for production scheduling. International Journal of Advanced Manufacturing Technology 36 (2008) 959-968
31. Chen Y.-W., Lu Y.-Z., and Yang G. Optimization with extremal dynamics for the traveling salesman problem. Physica A 385 (2007) 115-123
32. Chen M.-R., Lu Y.-Z., and Yang G. Multiobjective extremal optimization with applications to engineering design. Journal of Zhejiang University-SCIENCE A 8 (2007) 1905-1911
33. Chen M.-R., Lu Y.-Z., and Yang G. Multiobjective optimization using population-based extremal optimization. Journal of Neural Computing and Applications 7 (2008) 101-109
34. Chen M.-R., and Lu Y.-Z. A novel elitist multiobjective optimization algorithm: multiobjective extremal optimisation. European Journal of Operational Research 188 (2008) 637-651
35. Moser I., and Hendtlass T. Solving problems with hidden dynamics-comparison of extremal optimization and ant colony system. Proceedings of 2006 IEEE Congress on Evolutionary Computation (CEC'2006) (2006) 1248-1255
36. Chen M.-R., Lu Y.-Z., and Yang G. Population-based extremal optimization with adaptive Lévy mutation for constrained optimization. Proceedings of 2006 International Conference on Computational Intelligence and Security (CIS'06) (2006) 258-261
37. Shi X.H., Liang Y.C., Lee H.P., Lu C., and Wang L.M. An improved GA and a novel PSO-GA-based hybrid algorithm. Information Processing Letters 93 (2005) 255-261
38. Boettcher S., and Percus A.G. Optimization with extremal dynamics. Physical Review Letters 86 (2001) 5211-5214
39. Yao X., Liu Y., and Lin G. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3 (1999) 82-102
40. M. Molga, C. Smutnicki, Test functions for optimization needs, 2005. Available at http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf.
41. Srinivasan D., and Seow T.H. Particle swarm inspired evolutionary algorithm (PS-EA) for multi-criteria optimization problems. Evolutionary Multiobjective Optimization (2006), Springer, Berlin, Heidelberg pp. 147-165