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Entropic simplified swarm optimization for the task assignment problem
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DOI：10.1016/j.asoc.2017.04.030.png)
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Abstract
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The task assignment problem (TAP) aims to assign application tasks to a number of distributed processors in a computation system in order to increase the efficiency and effectiveness of the system for minimizing or maximizing a certain cost. The problem is NP-hard; thus, finding the exact solutions is computationally intractable for larger size problems. In this paper, a novel entropic simplified swarm optimization, known as ESSO, is proposed for solving this problem. In this method, an entropic local search (ELS) inspired by information theory is proposed to enhance the exploitation capability of SSO. Entropy is adopted to describe the uncertainty level of assigned tasks; the task with higher uncertainty then has more chance to be reassigned. Furthermore, for each reassigned task, the corresponding list of potential processors can be constructed using information theory; this enhances the probability of finding promising solutions in ELS. To empirically evaluate the performance of the proposed method, experiments are conducted using twenty-four randomly generated problems ranging from small to large scale, and the corresponding results are compared with existing works. The experiment results indicate that ESSO is better than its competitors in both solution quality and efficiency. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Task assignment problem
Simplified swarm optimization
Information theory
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