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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: kehanzeng1980@gmail.com
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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 

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