Particle Swarm Optimization Algorithm Based on Escape Boundary


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The particle swarm optimization (PSO) is a population-based stochastic evolutionary algorithm, noted for its capability of searching for the global optimum of complex problems. Particles flying out of the solution space will lead to invalid solutions. So often in engineering applications, boundary condition is used to confine the particles within the solution space. In this paper, a new boundary is proposed, which is called as escape boundary. The solution space is divided into three sections, that is, the inside,escape boundary and the outside of the boundary. The location of the global solution in the solution space, accordingly has two types, that is, the global optimum around the center of the solution space, and the global optimum close to the escape boundary. The proposed boundary is introduced into the PSO algorithm, and is compared to the damping boundary. The experimental results show that the PSO based on escape boundary has better search ability and faster convergence rate.



Advanced Materials Research (Volumes 361-363)

Edited by:

Qunjie Xu, Honghua Ge and Junxi Zhang




W. H. Han, "Particle Swarm Optimization Algorithm Based on Escape Boundary", Advanced Materials Research, Vols. 361-363, pp. 1426-1431, 2012

Online since:

October 2011





[1] J. Kennedy, R.C. Eberhart. Particle swarm optimization, IEEE International Conference on Neural Networks, Piscataway, NJ, 1995, 4: 1942-(1948).

[2] Ke Meng, Hong Gang Wang, ZhaoYang Dong, et al. Quantum-Inspired Particle Swarm Optimization for Valve-Point Economic Load Dispatch [J]. IEEE Transactions on Power Systems. 2010, 25(1): 215-222.


[3] Chien-Hung Liu, Yuan-Yih Hsu. Design of a Self-Tuning PI Controller for a STATCOM Using Particle Swarm Optimization [J]. IEEE Transactions on Industrial Electronics, 2010, 57(2): 702 - 715.


[4] Xiaodong Li. Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology [J]. IEEE Transactions on Evolutionary Computation, 2010, 14(1): 150-169.


[5] Biswal B, Dash P. K, Panigrahi B.K. Power Quality Disturbance Classification Using Fuzzy C-Means Algorithm and Adaptive Particle Swarm Optimization [J]. IEEE Transactions on Industrial Electronics, 2009, 56(1) : 212-220.


[6] Zielinski K, Weitkemper P, Laur R, Kammeyer K D. Optimization of Power Allocation for Interference Cancellation With Particle Swarm Optimization [J]. IEEE Transactions on Evolutionary Computation, 2009, 13 (1) : 128-150.


[7] Po-Hung Chen. Pumped-Storage Scheduling Using Evolutionary Particle Swarm Optimization [J]. IEEE Transaction on Energy Conversion, March 2008, 23 (1) : 294- 301.


[8] Jacob Robinson, Yahya Rahmat-Samii. Particle Swarm Optimization in Electromagnetics [J]. IEEE Transactions on Antennas and Propagation, 2004, 52(2) : 397-407.


[9] Jian Hu, Zhi-shu Li, Zhen Luo, et al. Invisible Wall in Particle Swarm Optimization [J]. Journal of Sichuan University (Engineering science editon). 2009, 41(5): 165-169 ( In Chinese).

[10] Tony Huang, Ananda Sanavarapu Mohan. A Hybrid Boundary Condition for Robust Particle Swarm Optimization [J]. IEEE Antennas and Wireless Propagation Letters. 2005, 4: 112-117.


[11] Shenheng Xu, Yahya Rahmat-Samii. Boundary Conditions in Particle Swarm Optimization Revisited [J]. IEEE Transactions on Antennas and Propagation, 2007, 55(3) : 760-765.


[12] W. Al-Hassan, M.B. Fayek, S.I. Shaheen. PSOSA: An Optimized Particle Swarm Technique for Solving the Urban Planning Problem. The 2006 International Conference on Computer Engineering and Systems, 2006, 401-405.


[13] R. C. Eberhart, Y. Shi, Particle swarm optimization: developments applications and resources, Proceedings of the 2001 Congress on Evolutionary Computation, 2001, 1: 81-86.


[14] F. V. D. Bergh. An analysis of particle swarmoptimizers, Ph.D. dissertation, Dept. Comput. Sci., Pretoria Univ., Pretoria, South Africa, (2001).