An Entropy-based PSO for DAR task scheduling problem

This paper addresses the task scheduling problem in the digital array radar (DAR), which determines the optimal execution order of all tasks subject to precedence and resource constraints. The aim is to achieve good performance in multiple aspects. To our best knowledge, the existing scheduling algorithms, neglecting the task internal structure, not posed as an optimization model, and only utilizing the heuristic method or the meta-heuristic method to solve the problem, cannot fully give free rein to the DAR capability of handling various tasks. Therefore, for such an N-P hard problem, an integer programming optimization model and a hybrid particle swarm optimization (PSO) algorithm are proposed. In the optimization model, a full radar task structure is established, and a comprehensive objective function is formed to guarantee the performance in multiple aspects. In the hybrid PSO, a modified PSO is incorporated to explore good scheduling schemes, and a heuristic task interleaving algorithm, embedded in the PSO framework, for the efficient task schedulability analysis. Moreover, the chaotic sequences are adopted to improve the quality of initialized solution. The Shannon's entropy is introduced to indicate the diversity of the population and adaptively tunes the parameters. Simulation results show that the proposed algorithm outperforms the three state-of-the-art scheduling algorithms while maintaining a reasonable runtime. (C) 2018 Elsevier B.V. All rights reserved.