Solving constrained optimization problems with hybrid particle swarm optimization

Engineering Optimization

Volumn 40, Issue 11, 2008, Pages 1031-1049

  Zahara, Erwie ^a   Hu, Chia Hsin ^a  

^a ST JOHN'S UNIVERSITY   (Taiwan)

Author keywords

Constrained optimization; Constraint handling; Nelder Mead simplex search method; Particle swarm optimization

Indexed keywords

CONSTRAINT THEORY; OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO); PROBABILITY DENSITY FUNCTION; PROBLEM SOLVING;

BENCHMARK PROBLEMS; CONSTRAINED OPTIMIZATION PROBLEMS; CONSTRAINT HANDLING; EFFICIENT; HEURISTICS; HYBRID PARTICLE SWARM OPTIMIZATIONS; LOCATING; NELDER-MEAD SIMPLEX SEARCH METHOD; NONLINEAR; OPTIMAL SOLUTIONS; PARTICLE SWARM OPTIMIZATION; RANKING METHODS; REAL WORLDS; REPAIR METHODS; SIMPLEX SEARCH METHODS;

CONSTRAINED OPTIMIZATION;

EID: 54949090033     PISSN: 0305215X     EISSN: 10290273     Source Type: Journal    
DOI: 10.1080/03052150802265870     Document Type: Article

References (18)

1. Becerra, R.L. and Coello, C.A., 2006. Cultured differential evolution for constrained optimization. Computer Methods in Applied Mechanics and Engineering, 195 (33-36), 4303-4322.
2. Chootinan, P. and Chen, A., 2006. Constraint handling in genetic algorithms using a gradient-based repair method. Computers and Operations Research, 33 (8), 2263-2281.
3. Coello Coello, C.A., 2002. Theoretical and numerical constraint handling techniques used with evolutionary algorithms; a survey of the state of art. Computer Methods in Applied Mechanics and Engineering, 191 (11-12), 1245-1287.
4. Dong, Y., Tang, J.F., Xu, B.D., and Wang, D., 2005. An application of swarm optimization to nonlinear programming. Computers and Mathematics with Applications, 49 (11-12), 1655-1668.
5. Eberhart, R.C. and Kennedy, J., 1995. A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 4-6 October, 39-43, IEEE Service Center.
6. Eberhart, R.C. and Shi, Y., 2001. Tracking and optimizing dynamic systems with particle swarms. Proceedings of Congress on Evolutionary Computation, Seoul, Korea, 27-30 May, 94-97, IEEE Service Center.
7. Fan, S.K. and Zahara, E., 2007. A hybrid simplex search and particle swarm optimization for unconstrained optimization. European Journal of Operational Research, 181 (2), 527-548.
8. Farmani, R. and Wright, J.A., 2003. Self-adaptive fitness formulation for constrained optimization. IEEE Transactions on Evolutionary Computation, 7 (5), 445-455.
9. Fung, R.Y.K., Tang, J.F., and Wang, D., 2002. Extension of ahybrid genetic algorithm for nonlinear programming problems with equality and inequality constraints. Computers and Operations Research, 29 (3), 261-274.
10. Gopal, A.V. and Rao, P.V., 2003. The optimization of the grinding of silicon carbide with diamond wheels using genetic algorithms. International Journal of Advanced Manufacturing Technology, 22 (7-8), 475-480.
11. Hedar, A.D. and Fukushima, M., 2006. Derivative-free filter simulated annealing method for constrained continuous global optimization. Journal of Global Optimization, 35 (4), 521-549.
12. Hu, X. and Eberhart, R.C., 2001. Tracking dynamic systems with PSO: where's the cheese? Proceedings of Workshop on Particle Swarm Optimization, Indianapolis, IN, USA Purdue School of Engineering and Technology.
13. Koziel, S. and Michaelwicz, Z., 1999. Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evolutionary Computation, 7(1), 19-44.
14. Lee, T.S., Ting, T.O., Lin, Y.J., and Htay, T., 2007. A particle swarm approach for grinding process optimization analysis. International Journal of Advanced Manufacturing Technology, 33 (11-12), 1128-1135.
15. Nash, S.G. and Sofer, A., 1996. Linear and nonlinear programming. New York: McGraw-Hill.
16. Nelder, LA. and Mead, R., 1965. A simplex method for function minimization. Computer Journal, 7, 308-313.
17. Runarsson, T.P. and Yao, X., 2005. Search biases in constrained evolutionary optimization. IEEE Transactions on Systems, Man and Cybernetics, 35 (2), 233-243.
18. Tang, J.F., Wang, D.W., Ip, A., and Fung, R. Y.K., 1998. A hybrid genetic algorithm for a type of non-linear programming problems. Computers and Mathematics with Applications, 36 (5), 11-21.