A New Particle Swarm Algorithm for Solving Constrained Optimization Problems

Abstract:

Considering that the particle swarm optimization (PSO) algorithm has a tendency to get stuck at the local solutions, an improved PSO algorithm is proposed in this paper to solve constrained optimization problems. In this algorithm, the initial particle population is generated using good point set method such that the initial particles are uniformly distributed in the optimization domain. Then, during the optimization process, the particle population is divided into two sub-populations including feasible sub-population and infeasible sub-population. Finally, different crossover operations and mutation operations are applied for updating the particles in each of the two sub-populations. The effectiveness of the improved PSO algorithm is demonstrated on three benchmark functions.

• Periodical: Advanced Materials Research (Volumes 734-737)
• Pages: 2875-2879
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.734-737.2875
• Online since: August 2013
• Authors: Tie Bin Wu*, Yun Cheng, Yun Lian Liu, Tao Yun Zhou, Xin Jun Li
• Keywords: Constrained Optimization, Crossover, Mutation, Particle Swarm Optimization (PSO)

References

[1] S. Kheawhom. Efficient constraint handling scheme for differential evolutionary algorithm in solving chemical engineering optimization problem[J]. Journal of Industrial and Engineering Chemistry, Vol. 16(2010), p.620.

DOI: 10.1016/j.jiec.2010.03.004

[2] Y. Wang, Z.X. Cai, Y.R. Zhou. Constrained optimization evolutionary algorithm[J]. Journal of Software. Vol. 20(2009), p.11

[3] H. Eskandar, A. Sadollah, A. Bahreininejad. Water cycle algorithm—A novel metaheuristic optimization method for solving constrained engineering optimization problems[J]. Computers and Structures, Vol. 110(2012), p.151

DOI: 10.1016/j.compstruc.2012.07.010

[4] M. Mezura, C. Coello. A simple multi-membered evolution strategy to solve constrained optimization problems[J]. IEEE Transactions on Evolutionary Computation, Vol. 9(2005), p.1

DOI: 10.1109/tevc.2004.836819

[5] T. Runarsson, X. Yao. Stochastic ranking for constrained evolutionary optimization[J]. IEEE Transactions on Evolutionary Computation, Vol. 4(2003), p.284

DOI: 10.1109/4235.873238

[6] Y. Wang, Z.X. Cai, Y.R. Zhou. Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique[J]. Structural and Multidisciplinary Optimization, Vol. 37(2009), p.395

DOI: 10.1007/s00158-008-0238-3