Adaptive particle swarm optimization for best schedule in algorithmic-level synthesis

This paper presents the adaptive particle swarm optimization for optimal scheduling in Algorithmic level synthesis. Adaptive Particle Swarm Optimization algorithm is executed on time constraint scheduling problem using Integer Linear Programming method. The adaptive evolutionary factor, adaptive inertia weight to update velocity equation is implemented. The experimental results evaluate the performance parameters for scheduling with comparison with adaptive particle swarm optimization(APSO) and particle swarm optimization (PSO). The optimal solution for differential equation solver adaptive particle swarm optimization outperforms scheduling optimally in few seconds, with few computations.