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Entropy 2017, 19(11), 596; https://doi.org/10.3390/e19110596

An Entropy-Based Adaptive Hybrid Particle Swarm Optimization for Disassembly Line Balancing Problems

School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, 333 Long Teng Road, Shanghai 201620, China
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Received: 8 August 2017 / Revised: 2 October 2017 / Accepted: 3 November 2017 / Published: 7 November 2017
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Abstract

In order to improve the product disassembly efficiency, the disassembly line balancing problem (DLBP) is transformed into a problem of searching for the optimum path in the directed and weighted graph by constructing the disassembly hierarchy information graph (DHIG). Then, combining the characteristic of the disassembly sequence, an entropy-based adaptive hybrid particle swarm optimization algorithm (AHPSO) is presented. In this algorithm, entropy is introduced to measure the changing tendency of population diversity, and the dimension learning, crossover and mutation operator are used to increase the probability of producing feasible disassembly solutions (FDS). Performance of the proposed methodology is tested on the primary problem instances available in the literature, and the results are compared with other evolutionary algorithms. The results show that the proposed algorithm is efficient to solve the complex DLBP. View Full-Text
Keywords: DLBP; DHIG; entropy; population diversity; AHPSO; dimension learning; crossover and mutation operator DLBP; DHIG; entropy; population diversity; AHPSO; dimension learning; crossover and mutation operator
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Xiao, S.; Wang, Y.; Yu, H.; Nie, S. An Entropy-Based Adaptive Hybrid Particle Swarm Optimization for Disassembly Line Balancing Problems. Entropy 2017, 19, 596.

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