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Parallel Hybrid PSO-GA Algorithm and Its Application to Layout Design

  • Guangqiang Li
  • Fengqiang Zhao
  • Chen Guo
  • Hongfei Teng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

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.

Keywords

Particle Swarm Optimization Layout Design Layout Problem Parallel Genetic Algo Slow Convergence Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Dowsland, K.A., Dowsland, W.B.: Packing Problems. European Journal of Operational Research 56, 2–14 (1992)MATHCrossRefGoogle Scholar
  2. 2.
    Qian, Z.Q., Teng, H.F.: Algorithms of Complex Layout Design Problems. China Mechanical Engineering 13, 696–699 (2002)Google Scholar
  3. 3.
    Davis, L.D.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)Google Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948 (1995)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    Shamir, R.: Efficiency of the Simplex Method: a Survey. Management Science 33, 301–334 (1987)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    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)MATHCrossRefGoogle Scholar
  12. 12.
    Li, G.Q.: Evolutionary Algorithms and their Application to Engineering Layout Design. Post-doctoral Research Report, Tongji University, Shanghai, China (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guangqiang Li
    • 1
  • Fengqiang Zhao
    • 2
  • Chen Guo
    • 1
  • Hongfei Teng
    • 3
  1. 1.College of Automation and Electrical EngineeringDalian Maritime UniversityDalianPeople’s Republic of China
  2. 2.College of Electromechanical & Information EngineeringDalian Nationalities UniversityDalianPeople’s Republic of China
  3. 3.School of Mechanical EngineeringDalian University of TechnologyDalianPeople’s Republic of China

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