Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions

被引：5

作者：

Basu M. ^{[1
]}

机构：

[1] Department of Power Engineering, Jadavpur University, Kolkata

来源：

Journal of The Institution of Engineers (India): Series B | 2016年 / 97卷 / 04期

关键词：

Gaussian random variable; Multimodal function; Particle swarm optimization; Unimodal function;

D O I：

10.1007/s40031-015-0204-6

中图分类号：

学科分类号：

摘要：

Particle swarm optimization (PSO) performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This paper proposes an improved particle swarm optimization (IPSO) which introduces Gaussian random variables in velocity term. This improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the speed of convergence and the simplicity of the structure of particle swarm optimization. The algorithm is experimentally validated on 17 benchmark functions and the results demonstrate good performance of the IPSO in solving unimodal and multimodal problems. Its high performance is verified by comparing with two popular PSO variants. © 2015, The Institution of Engineers (India).

引用

页码：525 / 535

页数：10

共 42 条

[1]

Eberhart R.C., Kennedy J., A new optimizer using particle swarm theory, in Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39-43, (1995)

[2]

Kennedy J., Eberhart R.C., Particle swarm optimization, in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942-1948, (1995)

[3]

Suganthan, Particle swarm optimizer with neighborhood operator, in Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1958-1962, (1999)

[4]

Ho S.Y., Lin H.S., Liauh W.H., Ho S.J., OPSO: orthogonal particle swarm optimization and its application to task assignment problems, IEEE Trans. Syst. Man Cybern. B, 38, 2, pp. 288-289, (2008)

[5]

Das T.K., Venayagamoorthy G.K., Aliyu U.O., Bio-inspired algorithms for the design of multiple optimal power system stabilizers: SPPSO and BFA, IEEE Trans. Ind. Appl., 44, 5, pp. 1445-1457, (2008)

[6]

del Valle Y., Venayagamoorthy G.K., Mohagheghi S., Hernandez J.-C., Harley R.G., Particle swarm optimization: basic concepts, variants and applications in power systems, IEEE Trans. Evol. Comput., 12, 2, pp. 171-195, (2008)

[7]

Wachowiak M.P., Smolikova R., Zheng Y., Zurada J.M., Elmaghraby A.S., An approach to multimodal biomedical image registration utilizing particle swarm optimization multimodal function optimization based on particle swarm optimization, IEEE Trans. Evol. Comput., 8, 3, pp. 289-301, (2004)

[8]

Shi Y., Eberhart R.C., A modified particle swarm optimizer, in Proceedings of the IEEE Congress on Evolutionary Computation, pp. 69-73, (1998)

[9]

Shi Y., Eberhart R.C., Empirical study of particle swarm optimization, in Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1945-1950, (1999)

[10]

Clerc M., Kennedy J., The particle swarm-explosion, stability and convergence in a multidimensional complex space, IEEE Trans. Evol. Comput., 6, 1, pp. 58-73, (2002)

← 1 2 3 4 5 →