Hybrid Particle Swarm Optimization Algorithm Based on Entropy Theory for Solving DAR Scheduling Problem

An efficient task-scheduling algorithm in the Digital Array Radar (DAR) is essential to ensure that it can handle a large number of requested tasks simultaneously. As a solution to this problem, in this paper, we propose an optimization model for scheduling DAR tasks using a hybrid approach. The optimization model considers the internal task structure and the DAR task-scheduling characteristic. The hybrid approach integrates a particle swarm optimization algorithm with a genetic algorithm and a heuristic task-interleaving algorithm. We introduce the chaos theory to optimize initialized particles and use entropy theory to indicate the diversity of particles and adaptively adjust the inertia weight, the crossover probability, and the mutation probability. Then, we improve both the efficiency and global exploration ability of the hybrid algorithm. In the framework of the swarm exploration algorithm, we include a heuristic task-interleaving scheduling algorithm, which not only utilizes the wait interval to transmit or receive subtasks, but also overlaps the receive intervals of different tasks. In a large-scale simulation, we demonstrate that the proposed algorithm is more robust and effective than existing algorithms.