Abstract
Packing and layout problems belong to NP-Complete problems theoretically and have found a wide utilization in practice. Parallel genetic algorithms (PGA) are relatively effective to solve these problems. But there still exist some defects of them, e.g. premature convergence and slow convergence rate. To overcome them, a parallel hybrid PSO-GA algorithm (PHPSO-GA) is proposed based on PGA. In PHPSO-GA, subpopulations are classified as several classes according to probability values of improved adaptive crossover and mutation operators. And in accordance with characteristics of different classes of subpopulations, different modes of PSO update operators are introduced. It aims at making full use of the fast convergence property of particle swarm optimization. Adjustable arithmetic-progression rank-based selection is introduced into this algorithm as well. It not only can prevent the algorithm from premature in the early stage of evolution but also can accelerate convergence rate in the late stage of evolution. To be hybridized with simplex method can improve local search performance. An example of layout design problem shows that PHPSO-GA is feasible and effective.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dowsland, K.A., Dowsland, W.B.: Packing Problems. European Journal of Operational Research 56, 2–14 (1992)
Qian, Z.Q., Teng, H.F.: Algorithms of Complex Layout Design Problems. China Mechanical Engineering 13, 696–699 (2002)
Davis, L.D.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)
Srinivas, M., Patnaik, L.M.: Adaptive Probabilities of Crossover and Mutation in Genetic Algorithm. IEEE Transaction on System Man and Cybernetics 24, 656–667 (1994)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948 (1995)
Eberhart, R., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)
Shi, Y., Eberhart, R.: A Modified Particle Swarm Optimizer. In: Proceedings of the IEEE Conference on Evolutionary Computation, Anchorage, AK, USA, pp. 69–73 (1998)
Eberhart, R., Shi, Y.: Particle Swarm Optimization: Developments, Applications and Resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, Seoul Korea, pp. 81–86 (2001)
Clerc, M.: The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, Washington, USA, vol. 3, pp. 1951–1957 (1999)
Shamir, R.: Efficiency of the Simplex Method: a Survey. Management Science 33, 301–334 (1987)
Teng, H.F., Sun, S.L., Liu, D.Q.: Layout Optimization for the Objects Located within a Rotating Vessel—a Three-dimensional Packing Problem with Behavioral Constraints. Computer & Operations Research 28, 521–535 (2001)
Li, G.Q.: Evolutionary Algorithms and their Application to Engineering Layout Design. Post-doctoral Research Report, Tongji University, Shanghai, China (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, G., Zhao, F., Guo, C., Teng, H. (2006). Parallel Hybrid PSO-GA Algorithm and Its Application to Layout Design. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_100
Download citation
DOI: https://doi.org/10.1007/11881070_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
eBook Packages: Computer ScienceComputer Science (R0)