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On the improvements of the particle swarm optimization algorithm. (English) Zbl 1182.65089

Summary: Since a particle swarm optimization (PSO) algorithm uses a coordinated search to find the optimum solution, it has a better chance of finding the global solution. Despite this advantage, it is also observed that some parameters used in PSO may affect the solution significantly. Following this observation, this research tries to tune some of the parameters and to add mechanisms to the PSO algorithm in order to improve its robustness in finding the global solution.
The main approaches include using uniform design to ensure uniform distribution of the initial particles in the design space, adding a mutation operation to increase the diversity of particles, decreasing the maximum velocity limitation and the velocity inertia automatically to balance the local and the global search efforts, reducing velocity when constraints are violated, and using Gaussian distribution based local searches to escape local minima. Besides these efforts, an algorithm is also developed to find multiple solutions in a single run. The results show that the overall effect of these approaches can yield better results for most test problems.

MSC:

65K05 Numerical mathematical programming methods
90C15 Stochastic programming
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[1] Kennedy J, Eberhart RC. Particle swarm optimization. In: Proceedings of IEEE International Conference in neural networks; 1995. p. 1942 – 8.
[2] Bergh, F. V. D.; Engelbrecht, A. P.: A study of particle swarm optimization particle trajectories, Inform sci 176, 937-971 (2006) · Zbl 1093.68105 · doi:10.1016/j.ins.2005.02.003
[3] Eberhart RC, Shi Y. A modified particle swarm optimizer. In: Proceedings of 1998 IEEE world congress on computational intelligence; 1998. p. 69 – 73.
[4] Fan, S. K.; Liang, Y. C.; Zahara, E.: Hybrid simplex search and particle swarm optimization for the global optimization of multimodal functions, Eng optimiz 36, No. 4, 401-418 (2004)
[5] He, S.; Prempain, E.; Wu, Q. H.: An improved practical swarm optimizer for mechanical design optimization problems, Eng optimiz 36, No. 5, 585-605 (2004)
[6] Bao, Z.; Hongfei, T.: Particle swarm optimization based on pyramid model for satellite module layout, Chin J mech eng 18, No. 4, 530-536 (2005)
[7] Liang, J. J.; Qin, A. K.; Suganthan, P. N.; Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE trans evolut comput 10, 281-295 (2006)
[8] Fang, K. T.; Wang, Y.; Bentler, P. M.: Some applications of number-theoretic methods in statistics, Stat sci 9, 416-428 (1994) · Zbl 0955.62620 · doi:10.1214/ss/1177010392
[9] Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micromachine and human science, Nagoya, Japan; 1995. p. 39 – 43.
[10] Fourie, P. C.; Groenwold, A. A.: The particle swarm optimization algorithm in size and shape optimization, Struct multidiscip optimiz 23, No. 4, 259-267 (2002)
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