By Krzysztof Socha, Joshua Knowles, Michael Sampels (auth.), Marco Dorigo, Gianni Di Caro, Michael Sampels (eds.)
This publication constitutes the refereed lawsuits 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 rigorously reviewed and chosen from fifty two submissions. The papers care for theoretical and foundational facets and numerous new editions of ant algorithms in addition to with a vast number of optimization purposes in networking and operations study. All in all, this booklet offers the state-of-the-art in examine and improvement within the rising box of ant algorithms
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Additional info for Ant Algorithms: Third International Workshop, ANTS 2002 Brussels, Belgium, September 12–14, 2002 Proceedings
ACO Applied to Group Shop Scheduling 23 Table 1. Overview on the diﬀerent MMAS versions. Identiﬁer U R ER ED EDI EDIT S 5 Characteristics MMAS using uniform setting of pheromone values MMAS using random setting of pheromone values MMAS using elite solutions and random setting of pheromone values MMAS using elite solutions, random setting for the ﬁrst three restart phases, and after that the diversiﬁcation setting of pheromone values MMAS using elite solutions, random setting for the ﬁrst three restart phases, and after that ﬂipping a coin in every restart phase to choose among diversiﬁcation setting and intensiﬁcation setting The same as EDI with an additional short Tabu Search run for the best ant in every iteration Comparison To test the usefulness of the intensiﬁcation and diversiﬁcation approaches outlined in the previous section we run experiments on the ﬁve diﬀerent versions of the Algorithm 1 outlined in the previous sections.
For example, a machine might be processing a job of type A and have only one job in its queue that is of type B. That means that, as long as the job of type A is not ﬁnished, the corresponding threshold values are decreased and the threshold values that are related to jobs of type B are increased. If the machine is oﬀered two jobs, one of type A and one of type B, the probability to take the job of type A would be higher than the probability to pick the other job. This behaviour is not desirable as it causes an additional setup.
The search. The results presented show the usefulness of our diversiﬁcation and intensiﬁcation approach. The results also show that our algorithm can be considerably improved by applying a very short run of Tabu Search to the 26 C. Blum iteration best ant. Our algorithm ﬁnds for OSP as well as for JSP instances the optimal solution. In the future we intend to further examine the possibilities of guiding the search of an ACO algorithm by additional diversiﬁcation and intensiﬁcation mechanisms. Acknowledgments.