Volume 16, Issue 4
Simulated Annealing for the 0/1 Multidimensional Knapsack Problem
DOI:

Numer. Math. J. Chinese Univ. (English Ser.)(English Ser.) 16 (2007), pp. 320-327

Published online: 2007-11

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• Abstract

In this paper a simulated annealing (SA) algorithm is presented for the $0/1$ multidimensional knapsack problem. Problem-specific knowledge is incorporated in the algorithm description and evaluation of parameters in order to look into the performance of finite-time implementations of SA. Computational results show that SA performs much better than a genetic algorithm in terms of solution time, whilst having a modest loss of solution quality.

• Keywords

@Article{NM-16-320, author = { F. B. Qian and R. Ding}, title = {Simulated Annealing for the 0/1 Multidimensional Knapsack Problem}, journal = {Numerical Mathematics, a Journal of Chinese Uniersities}, year = {2007}, volume = {16}, number = {4}, pages = {320--327}, abstract = { In this paper a simulated annealing (SA) algorithm is presented for the $0/1$ multidimensional knapsack problem. Problem-specific knowledge is incorporated in the algorithm description and evaluation of parameters in order to look into the performance of finite-time implementations of SA. Computational results show that SA performs much better than a genetic algorithm in terms of solution time, whilst having a modest loss of solution quality.}, issn = {}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/nm/8060.html} }
TY - JOUR T1 - Simulated Annealing for the 0/1 Multidimensional Knapsack Problem AU - F. B. Qian & R. Ding JO - Numerical Mathematics, a Journal of Chinese Uniersities VL - 4 SP - 320 EP - 327 PY - 2007 DA - 2007/11 SN - 16 DO - http://dor.org/ UR - https://global-sci.org/intro/nm/8060.html KW - AB - In this paper a simulated annealing (SA) algorithm is presented for the $0/1$ multidimensional knapsack problem. Problem-specific knowledge is incorporated in the algorithm description and evaluation of parameters in order to look into the performance of finite-time implementations of SA. Computational results show that SA performs much better than a genetic algorithm in terms of solution time, whilst having a modest loss of solution quality.