By Krzysztof Socha, Joshua Knowles, Michael Sampels (auth.), Marco Dorigo, Gianni Di Caro, Michael Sampels (eds.)
This e-book constitutes the refereed complaints of the 3rd foreign Workshop on Ant Algorithms, ANTS 2002, held in Brussels, Belgium in September 2002.
The 17 revised complete papers, eleven brief papers, and prolonged poster abstracts provided have been conscientiously reviewed and chosen from fifty two submissions. The papers care for theoretical and foundational facets and various new variations of ant algorithms in addition to with a vast number of optimization purposes in networking and operations learn. All in all, this booklet provides the state-of-the-art in learn and improvement within the rising box of ant algorithms
Read or Download Ant Algorithms: Third International Workshop, ANTS 2002 Brussels, Belgium, September 12–14, 2002 Proceedings PDF
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Additional resources for Ant Algorithms: Third International Workshop, ANTS 2002 Brussels, Belgium, September 12–14, 2002 Proceedings
M. Voigt, W. Ebeling, I. -P. Schwefel, editors, Proceedings of the 4th Conference on Parallel Problem Solving from Nature – PPSN IV, volume 1411 of Lecture Notes in Computer Science, pages 178–187, Berlin, 1996. Springer. 15. E. Nowicki and C. Smutnicki. A fast taboo search algorithm for the job-shop problem. Management Science, 42(2):797–813, 1996. 16. M. E. Goldberg, and F. Lobo. A survey of optimization by building and using probabilistic models. Technical Report No. 99018, IlliGAL, University of Illinois, 1999.
28–39, 2002. 1). Each time a machine changes the kind of job it performs, a setup is required, which is related to a ﬁxed cost. Machines automatically adapt to the appearances of jobs in the environment in order to minimize the number of setups and to maximize the number of ﬁnished jobs. To achieve this we take inspiration from social insects like ants or wasps. e. diﬀerent activities are often performed simultaneously by specialized individuals. This methodology is believed to be more eﬃcient than sequential task performance by unspecialized workers as it avoids unnecessary task switching, which costs time and energy.
The thresholds should not immediately be set to a minimum value as this would cause additional setups. It is advantageous that a machine stays idle for a short amount of time instead of taking any job, as a setup determines a very high cost. Dominance Contest. The dominance contest is used as a rule to decide between several machines in case more than one machines tries to take the same job. In this case a dominance contest is held and the winner is assigned the job. For this, each participant is given a force value F : Fa = 1 + Tp,a + Ts,a , (7) where Tp,a is the sum of the processing times of the jobs in the queue of machine a and Ts,a is the sum of the setup times.
Ant Algorithms: Third International Workshop, ANTS 2002 Brussels, Belgium, September 12–14, 2002 Proceedings by Krzysztof Socha, Joshua Knowles, Michael Sampels (auth.), Marco Dorigo, Gianni Di Caro, Michael Sampels (eds.)