Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-23T14:05:59.442Z Has data issue: false hasContentIssue false
  • English
  • Français

Probability increment based swarm optimization for combinatorial optimization with application to printed circuit board assembly

Published online by Cambridge University Press:  05 February 2014

Kehan Zeng ,
Zhen Tan ,
Mingchui Dong  and
Ping Yang
Kehan Zeng*
Affiliation:
Faculty of Science and Technology, University of Macau, Avenida Padre Tomas Pereira, Taipa, Macau Sar, China Department of Computer Science, Huizhou University, Huizhou, Guangdong, China
Zhen Tan
Affiliation:
Faculty of Science and Technology, University of Macau, Avenida Padre Tomas Pereira, Taipa, Macau Sar, China
Mingchui Dong
Affiliation:
Faculty of Science and Technology, University of Macau, Avenida Padre Tomas Pereira, Taipa, Macau Sar, China
Ping Yang
Affiliation:
School of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
*
Reprint requests to: Kehan Zeng, Room 106, Block 3, University of Macau, Avenida Padre Tomas Pereira, Taipa, Macau Sar, China. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

A novel swarm intelligence approach for combinatorial optimization is proposed, which we call probability increment based swarm optimization (PIBSO). The population evolution mechanism of PIBSO is depicted. Each state in search space has a probability to be chosen. The rule of increasing the probabilities of states is established. Incremental factor is proposed to update probability of a state, and its value is determined by the fitness of the state. It lets the states with better fitness have higher probabilities. Usual roulette wheel selection is employed to select states. Population evolution is impelled by roulette wheel selection and state probability updating. The most distinctive feature of PIBSO is because roulette wheel selection and probability updating produce a trade-off between global and local search; when PIBSO is applied to solve the printed circuit board assembly optimization problem (PCBAOP), it performs superiorly over existing genetic algorithm and adaptive particle swarm optimization on length of tour and CPU running time, respectively. The reason for having such advantages is analyzed in detail. The success of PCBAOP application verifies the effectiveness and efficiency of PIBSO and shows that it is a good method for combinatorial optimization in engineering.

Keywords

Combinatorial OptimizationPopulation EvolutionPrinted Circuit Board AssemblyState ProbabilitySwarm Intelligence
Type
Regular Articles
Information
AI EDAM , Volume 28 , Issue 4: Design of Complex Engineered Systems , November 2014 , pp. 429 - 437
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Beni, G., & Wang, J. (1988). The concept of cellular robotic system. Proc. IEEE Int. Symp. Intelligent Control, Los Altimos, CA.Google Scholar
Beni, G., & Wang, J. (1989). Swarm intelligence in cellular robotic systems. Proc. NATO Advance Workshop on Robots and Biological Systems, Tuscany, Italy.Google Scholar
Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. New York: Oxford University Press.CrossRefGoogle Scholar
Chen, Y., & Lin, C. (2007). A particle swarm optimization approach to optimize component placement in printed circuit board assembly. International Journal of Advanced Manufacturing Technology 35(5–6), 909–925.Google Scholar
Eberhart, R.C., & Shi, Y. (2007). Computational Intelligence: Concepts to Implementations. Burlington, VT: Morgan Kaufmann.Google Scholar
Ho, W., & Ji, P. (2007). Optimal Production Planning for PCB Assembly. London: Springer–Verlag.Google Scholar
Jin, Y., & Li, W. (2007). Design concept generation: a hierarchical coevolutionary approach. Journal of Mechanical Design 129, 1012–1022.CrossRefGoogle Scholar
Jin, Y., Li, W., & Lu, S.C.Y. (2005). A hierarchical co-evolutionary approach to conceptual design. CIRP Annals Manufacturing Technology 54(1), 155–158.Google Scholar
Jin, Y., Zouein, G.E., & Lu, S.C.Y. (2009). A synthetic DNA based approach to design of adaptive systems. CIRP Annals—Manufacturing Technology 54(1), 153–156.Google Scholar
Khoo, L.P., & Ng, T.K. (1998). A genetic algorithm-based planning system for PCB component placement. International Journal of Production Economics 54(3), 321–332.Google Scholar
Koenig, R., & Schneider, S. (2012). Hierarchical structuring of layout problems in an interactive evolutionary layout system. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26(2), 129–142.Google Scholar
Korte, B., & Vygen, J. (2008). Combinatorial Optimization: Theory and Algorithms. Berlin: Springer.Google Scholar
Lipson, H. (2008). Evolutionary synthesis of kinematic mechanisms. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22, 129–142.CrossRefGoogle Scholar
Loh, T.S., Bukkapatnam, S.T.S., Medeiros, D., & Kwonb, H. (2001). A genetic algorithm for sequential part assignment for PCB assembly. Computer & Industrial Engineering 40(4), 293–307.Google Scholar
Maher, M.L., & Fisher, D.H. (2012). Using AI to evaluate creative designs. Proc. Int. Conf. Creative Design, ICDC'2012. Glasgow: Design Society.Google Scholar
Najera, A.G., & Brizuela, C.A. (2005). PCB assembly: an efficient genetic algorithm for slot assignment and component pick and place sequence problems. Proc. IEEE Congr. Evolutionary Computation CEC'05. Edinburgh.Google Scholar
Shea, K., Ertelt, C., Gmeiner, T., & Ameri, F. (2010). Design-to-fabrication automation for the cognitive machine shop. Advanced Engineering Informatics 24(3), 251–268.CrossRefGoogle Scholar
Wang, J.F., Liu, J.H., Li, S.Q., & Zhong, Y.F. (2003). Intelligent selective disassembly using the ant colony algorithm. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17, 325–333.Google Scholar
Yang, P., & Zeng, K. (2009). A high-performance approach on mechanism isomorphism identification based on an adaptive hybrid genetic algorithm for digital intelligent manufacturing. Engineering With Computers 25(4), 397–403.Google Scholar