By David W. Corne, Martin J. Oates, George D. Smith
Written in an available and easy-to-read variety, this state-of-the-art publication provides complicated suggestions to present and destiny telecommunications optimization difficulties. the sector of telecommunications is starting to be and altering ever extra speedily, offering new real-world difficulties for optimization researchers to handle. Telecommunications engineers are inclined to recognize all concerning the difficulties concerned yet are usually now not conscious of advancements in computing device technological know-how and synthetic intelligence that will resolve these difficulties. This specified e-book takes a colloborative technique describing the essence of the issues after which the heuristic and adaptive innovations that are now recognized as adept at fixing those difficulties. furthermore the rising applied sciences in telecommunications and lengthening use of the web extend the function that complicated heuristic and adaptive tools can play.
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Extra resources for Telecommunications optimization: heuristics and adaptive techniques
Example text
2. Compute T, the maximum spanning tree of R. 3. Convert T into a linear flow-equivalent graph, L, by using Algorithm 1 below. 4. Factor L into a set of uniform capacity rings, as described in Frank and Frisch (1971). 5. Superimpose the set of rings from step 3 to form a network topology, N. 6. Remove any short cut edges in N which are not cost efficient, and re-route their flow on other edges in N. This step produces the final network topology, NΝ. Steps 1 and 2 determine the dominant requirements of the problem.
This replicated a given system t = 25000 times. This was used to help ensure the feasibility and accuracy of the very best candidate designs. 25, rp = 6, and gmax = 1200. 24 × 1027 possible designs, it was impossible to enumerate. So, a random greedy search was used as a comparison. 4. 6. 4 that the GA best cost dips much more rapidly than does the best cost corresponding to the greedy algorithm, indicating that the GA will find good solutions much more efficiently than a myopic approach. Also, both lines appear to be asymptotically approaching a solution, however the line corresponding to the GA is approaching a much better solution than the line corresponding to the greedy search.
The 42 Telecommunications Optimization: Heuristic and Adaptive Techniques significance of a minimum weight flow-equivalent graph is that, if the cost to send one unit of flow over any edge is one (a unit cost condition), then the minimum weight graph is also a minimum cost graph, and is optimal under unit cost conditions. Another appealing property of the Minimum Weight Flow-Equivalent Graph (MWFEG) is that it is, at least, bi-connected (thus solving problem 1 above). If the MWFEG can be modified to allow for concurrent use of the graph by multiple commodities, and accommodate a more robust cost function, it may produce near-optimum results.
