A binary particle swarm optimization based on the surrogate information with proportional acceleration coefficients for the 0-1 multidimensional knapsack problem

勤益科大機構典藏 > 管理學院 > 資訊管理系(研發與科技管理研究所) > 【資訊管理系】期刊論文 > Item 987654321/5907

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題名:	A binary particle swarm optimization based on the surrogate information with proportional acceleration coefficients for the 0-1 multidimensional knapsack problem
作者:	Lin, Chin-Jung Chern, Maw-Sheng Chih, Mingchang
貢獻者:	圖書館
關鍵詞:	0-1 Multidimensional knapsack problem OR-library binary particle swarm optimization propotional acceleration coefficients surrogate ratio
日期:	2016-02-17
上傳時間:	2016-10-18 15:53:50 (UTC+8)
出版者:	Taylor and Francis Ltd
摘要:	The 0-1 multidimensional knapsack problem (MKP) has been proven it belongs to difficult NP-hard combinatorial optimization problems. There are various search algorithms based on population concept to solve these problems. The particle swarm optimization (PSO) technique is adapted in our study, which proposes a novel PSO algorithm, namely, the binary PSO based on surrogate information with proportional acceleration coefficients (BPSOSIPAC). The proposed algorithm was tested on 135 benchmark problems from the OR-Library to validate and demonstrate the efficiency in solving multidimensional knapsack problems. The results were then compared with those in the other nine existing PSO algorithms. The simulation and evaluation results showed that the proposed algorithm, BPSOSIPAC, is superior to the other methods according to success rate, average number of function evaluations, average number of function evaluations of successful runs, average error (AE), mean absolute deviation, mean absolute percentage error, least error, standard deviation, best profit, mean profit, worst profit, AE of best profit (%), and AE of mean profit deviation (%).
關聯:	Journal of Industrial and Production Engineering, Volume 33, Number 2, 17 February 2016, pp. 77-102(26)
顯示於類別:	[資訊管理系(研發與科技管理研究所)] 【資訊管理系】期刊論文

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