Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/75471


Title: A hybrid particle swarm optimization algorithm with diversity for flow-shop scheduling problem
Authors: Chen, Chuenlung
陳春龍
Contributors: 資訊管理學系
Keywords: Attractive and repulsive particle swarm optimization (ARPSO);Diversity-guided search;Encoding schemes;Flow shop scheduling problem;Guided search;Hybrid particle swarm optimization algorithm;Hybrid PSO;Makespan;Premature convergence;Scheduling algorithms;Particle swarm optimization (PSO)
Date: 2009-12
Issue Date: 2015-06-01 17:24:26 (UTC+8)
Abstract: This paper proposed a hybrid particle swarm optimization algorithm (Shadow hybrid PSO, SHPSO) to solve the flow-shop scheduling problem (FSSP). SHPSO adopts the idea of Kuoa's HPSO model[4] by not only combines the random-key (RK) encoding scheme, individual enhancement (IE) scheme, but also adds the diversification mechanism such as ARPSO model and competitive shadow particles to prevent premature convergence. Computation experiments results of Taillard's [10] seven representative instances of FSSP show that the SHPSO perform close to HPSO for FSSP to minimize makespan. Further recommendations and improved ideas will be discussed later in this paper. © 2009 IEEE.
Relation: 2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009,864-867
Data Type: conference
DOI 連結: http://dx.doi.org/10.1109/ICICIC.2009.21
Appears in Collections:[資訊管理學系] 會議論文

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