By Krzysztof Socha, Joshua Knowles, Michael Sampels (auth.), Marco Dorigo, Gianni Di Caro, Michael Sampels (eds.)
This publication constitutes the refereed complaints of the 3rd overseas 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 awarded have been conscientiously reviewed and chosen from fifty two submissions. The papers take care of theoretical and foundational points and numerous new editions of ant algorithms in addition to with a large number of optimization purposes in networking and operations learn. All in all, this booklet offers 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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Extra resources for Ant Algorithms: Third International Workshop, ANTS 2002 Brussels, Belgium, September 12–14, 2002 Proceedings
That is, the insect agent decides whether its machine wants to get engaged in a certain job or not. The jobs are created dynamically: each task has a certain probability to appear at each time step. The main values to measure the performance are the number of setups and the number of ﬁnished jobs. The number of setups is crucial as it is linked to a high cost in time. Additionally, in a factory environment a setup can cause a monetary cost, that is not considered in this paper. We assume a setup time to be twice the processing time of a job.
20,22]). , [13,12]). In contrast to that, most of the intensiﬁcation and diversiﬁcation mechanisms to be outlined in the following are based on a set of elite solutions found by the algorithm in the history of the search process. As a ﬁrst strategy to introduce more intensiﬁcation and diversiﬁcation into the the search process performed by Algorithm 1 we changed the functions to initialize and reset the pheromone values. 757 . This introduces more intensiﬁcation, because 7 Note that the Hyper–Cube framework facilitates a strategy like that, as we explicitly know the space in which the pheromone values move.
Technical Report TR/IRIDIA/2001-15, IRIDIA, Universit´e Libre de Bruxelles, Belgium, 2001. Agent-Based Approach to Dynamic Task Allocation Shervin Nouyan IRIDIA, Universit´e Libre de Bruxelles CP 194/6, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium Lehrstuhl f¨ ur Nachrichtentechnik, Technische Universit¨ at M¨ unchen Arcisstr. be Abstract. We investigate a multi-agent algorithm inspired by the task allocation behavior of social insects for the solution of dynamic task allocation problems in stochastic environments.