優加劣減式仿頻寬限制資料傳輸優化演算法

林子鑑

doi:10.6342/NTU201704203

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優加劣減式仿頻寬限制資料傳輸優化演算法

Segment-Discriminated Bandwidth Restricted Transmission-Simulated Optimization Algorithm

林子鑑 , Masters　　Advisor：楊烽正　　

繁體中文 DOI： 10.6342/NTU201704203

啟發式演算法；仿頻寬限制資料傳輸優化演算法；排程；旅行銷售員問題；裝箱問題； heuristic algorithm ； bandwidth restricted transmission-simulated optimization algorithm ； scheduling ； traveling salesman problems ； bin packing problems

Abstract │ Reference (11) │ Altmetrics

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This research presents an innovative heuristic algorithm called “Segment-Discriminated Bandwidth Restricted Transmission-Simulated Optimization Algorithm” (SD-BRT) for solving discrete optimization problems. SD-BRT improves “Bandwidth Restricted Transmission-Simulated Optimization Algorithm”(original BRT).Like original BRT, SD-BRT imitates the behavior of data transmission over the network and simulates messengers transmit messages. Messengers select the communication links and constructive a complete route under the restriction of resources. The communication links are subject to operations of natural deterioration and enhancement/deduction bandwidth. SD-BRT improves original BRT in operations of enhancement/deduction bandwidth with Segment-Discriminated way. We develop SD-BRT solving system for Bin Packing Problem(BPP) and Traveling Salesman Problem(TSP) and compare results with original BRT.
Also, this research defines a scheduling problem called “Resource Shared Scheduling Problem”(RSSP). In RSSP, one “job” should be processed once by one “machine”. Before processes a job, the machine should setup a “resource” for the job. Every job has a “resource type”. If there are two jobs sequentially processed by the same machine and the two jobs has the same resource type. Then we call the two jobs “Resource Shared”, and the second job do not need to setup the resource. This research defines RSSP completely, and develop a SD-BRT solving system to solve RSSP.
In solving BPP and JSP, the results prove that SD-BRT is better than original BRT. Also the results indicate that SD-BRT is capable for solving RSSP.

Reference ( 11 ) 〈TOP〉

Dorigo, M., and Gambardella, L. M. (1997), “Ant Colony System: A cooperative Learning Approach to the Traveling Salesman Problem,” IEEE Transactions on Evolutionary Computation, vol.1, no.1, pp. 53-66.
連結：
Garey, M., Graham, R., Johnson, D., and Yao, A. (1976), “Resource Constrained Scheduling as Generalized Bin Packing,” J. Comb. Theory, Ser. A 21.
連結：
Holland, J.H. (1975), “Adaptation in Natural and Artificial System,” The University of Michigan Press.
連結：
Scholl, A., Klein, R., and Jurgens, C. (1997), “ BISON: a fast hybrid procedure for exactly solving the one-dimensional bin packing problem,” Computers & Operations Research 24, 627-645.
連結：
Ugur, A., and Aydim, D. (2009), “An interactive simulation and analysis software for solving TSP using Ant Colony Optimization algorithms,” Advances in Engineering Software, Volume 40, Issue 5, May 2009, Pages 341-349
連結：

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