By Wen Peng, Ruofeng Tong, Guiping Qian, Jinxiang Dong (auth.), De-Shuang Huang, Kang Li, George William Irwin (eds.)

ISBN-10: 3540372776

ISBN-13: 9783540372776

ISBN-10: 3540372822

ISBN-13: 9783540372820

The foreign convention on clever Computing (ICIC) was once shaped to supply an annual discussion board devoted to the rising and not easy subject matters in synthetic intelligence, desktop studying, bioinformatics, and computational biology, and so forth. It goals to collect researchers and practitioners from either academia and to percentage principles, difficulties and suggestions relating to the multifaceted elements of clever computing. ICIC 2006 held in Kunming, Yunnan, China, August 16-19, 2006, was once the second one foreign convention on clever Computing, outfitted upon the luck of ICIC 2005 held in Hefei, China, 2005. This yr, the convention targeted generally at the theories and methodologies in addition to the rising purposes of clever computing. It meant to unify the modern clever computing recommendations inside an vital framework that highlights the tendencies in complex computational intelligence and bridges theoretical learn with functions. particularly, bio-inspired computing emerged as having a key position in pursuing for novel know-how lately. The ensuing suggestions vitalize lifestyles technology engineering and lifestyle functions. In gentle of this pattern, the topic for this convention was once “Emerging clever Computing know-how and Applications”. Papers concerning this topic have been specially solicited, together with theories, methodologies, and functions in technological know-how and technology.

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Extra resources for Computational Intelligence and Bioinformatics: International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August 16-19, 2006. Proceedings, Part III

Sample text

4 Conclusion The rule derived from the results of TSP instances can summarize as follows: First, select proper values of Į and ȕ at the beginning because some combinations of Į and ȕ are invalid to obtain the global best solution. Second, the value of ȡ can influence convergence rate. Don’t select the value of ȡ solely. For example, keeping an eye on the value of q0 is necessary. Third, the performance of using two selection methods in the algorithm is better than using single one. And when ȕĺ5, the value of q0 can be selected in a bigger range and it is shown that the roulette selection method is more effective during the process.

In this paper, Į and ȕ are researched first for the random proportion rule has higher priority than other rules and must be treated first in the algorithm. Table 2. Feasible set of combinations of Į and ȕ in eli51 (a 51 cities TSP instance). Good combinations. : Poor combinations. : Bad combinations. The algorithm runs 10 times for each set of parameters. 0 Study of Parametric Relation in ACO Approach to Traveling Salesman Problem 27 Table 3. Feasible set of combinations of Į and ȕ in korA100 (a 100 cities TSP problem).

This paper describes the importance and functioning of α and β , and draws a conclusion that a fixed β may not enable ACO to use both heuristic and pheromone information for solution when α = 1 . Later, following the analysis, an adaptive β strategy is designed for improvement. Finally, a new ACO called adaptive weight ant colony system (AWACS) with the adaptive β and α = 1 is introduced, and proved to be more effective and steady than traditional ACS through the experiment based on TSPLIB test.

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Computational Intelligence and Bioinformatics: International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August 16-19, 2006. Proceedings, Part III by Wen Peng, Ruofeng Tong, Guiping Qian, Jinxiang Dong (auth.), De-Shuang Huang, Kang Li, George William Irwin (eds.)


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