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http://dx.doi.org/10.9723/jksiis.2022.27.4.037

Multi Agents-Multi Tasks Assignment Problem using Hybrid Cross-Entropy Algorithm  

Kim, Gwang (조선대학교 경영학부)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.27, no.4, 2022 , pp. 37-45 More about this Journal
Abstract
In this paper, a multi agent-multi task assignment problem, which is a representative problem of combinatorial optimization, is presented. The objective of the problem is to determine the coordinated agent-task assignment that maximizes the sum of the achievement rates of each task. The achievement rate is represented as a concave down increasing function according to the number of agents assigned to the task. The problem is expressed as an NP-hard problem with a non-linear objective function. In this paper, to solve the assignment problem, we propose a hybrid cross-entropy algorithm as an effective and efficient solution methodology. In fact, the general cross-entropy algorithm might have drawbacks (e.g., slow update of parameters and premature convergence) according to problem situations. Compared to the general cross-entropy algorithm, the proposed method is designed to be less likely to have the two drawbacks. We show that the performances of the proposed methods are better than those of the general cross-entropy algorithm through numerical experiments.
Keywords
Agent-Task assignment problem; Cross-Entropy algorithm; Kullback-Leibler divergence; Combinatorial optimization;
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