Computing the minimum initial marking (MIM) in labeled Petri nets (PN) while considering a sequence of labels constitutes a difficult problem. The existing solutions of such a problem suffer from diverse limitations. ...
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ISBN:
(纸本)9781728158235
Computing the minimum initial marking (MIM) in labeled Petri nets (PN) while considering a sequence of labels constitutes a difficult problem. The existing solutions of such a problem suffer from diverse limitations. In this paper, we proposed a new approach to automatically compute the MIM in labeled PNs in a timely fashion. We adopted a grasp (Greedy Randomized Adaptive Search Procedure)-based algorithm to model the MIM problem. The choice of such an algorithm is justified by the nature of the MIM process which belongs to the NP-hard class. We experimentally showed the effectiveness of our approach and empirically studied the initial marking quality in particular.
We introduce BubbleScarch, a general approach for extending priority-based greedy heuristics. Following the framework recently developed by Borodin et al., we consider priority algorithms, which sequentially assign va...
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We introduce BubbleScarch, a general approach for extending priority-based greedy heuristics. Following the framework recently developed by Borodin et al., we consider priority algorithms, which sequentially assign values to elements in some fixed or adaptively determined order. BubbleSearch extends priority algorithms by selectively considering additional orders near an initial good ordering. While many notions of nearness are possible, we explore algorithms based on the Kendall-tau distance (also known as the BubbleSort distance) between permutations. Our contribution is to elucidate the BubbleSearch paradigm and experimentally demonstrate its effectiveness. (c) 2005 Published by Elsevier B.V.
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