A FUZZY EVOLUTIONARY ALGORITHM FOR TOPOLOGY DESIGN OF CAMPUS NETWORKS
ABSTRACT The topology design of campus networks is a hard constrained combinatorial optimization problem. It consists of deciding the number, type, and location of the active network elements (nodes), and the links. This choice is dictated by physical and technological constraints and must optimize...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| التنسيق: | article |
| منشور في: |
2020
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://eprints.kfupm.edu.sa/id/eprint/265/1/J_Youssef_AJSE_October2004.pdf |
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| الملخص: | ABSTRACT The topology design of campus networks is a hard constrained combinatorial optimization problem. It consists of deciding the number, type, and location of the active network elements (nodes), and the links. This choice is dictated by physical and technological constraints and must optimize several objectives. Important objectives are monetary cost, network delay, hop count between communicating pairs, and reliability. Furthermore, due to the nondeterministic nature of network traffic and other design parameters, the objective criteria are imprecise. Fuzzy Logic provides a suitable mathematical framework in such a situation. In this paper, we present a Simulated Evolution algorithm for the design of campus network topology. To intensify the search, we have also incorporated Tabu Search-based characteristics in the allocation phase of the SE algorithm. The proposed fuzzy SE algorithm is compared with the Simulated Annealing heuristic. Comparison is also made with Esau–Williams (EW) algorithm, a well known constructive algorithm for the category of problems addressed in this work. Results show that on all test cases, the Simulated Evolution algorithm exhibits a more intelligent search of the solution subspace and was able to find better solutions than Simulated Annealing and Esau–Williams algorithm. Keywords: Campus Networks, Combinatorial Optimization, Fuzzy Logic, Iterative Heuristics, Network Topology, Simulated Annealing, Simulated Evolution, Tabu Search. |
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