Particle Swarm Optimization of Memory usage in Embedded Systems

In this paper, we propose a dynamic, non-dominated sorting, multi-objective particle-swarm-based optimizer, named Hierarchical Non-dominated Sorting Particle Swarm Optimizer (H-NSPSO), for memory usage optimization in embedded systems. It significantly reduces the computational complexity of others Multi-Objective Particle Swarm Optimization (MOPSO) algorithms. Concretely, it first uses a fast non-dominated sorting approach with O(mN^2) computational complexity. Second, it maintains an external archive to store a fixed number of non-dominated particles, which is used to drive the particle population towards the best non-dominated set over many iteration steps. Finally, the proposed algorithm separates particles into multi sub-swarms, building several tree networks as the neighborhood topology. HNSPSO has been made adaptive in nature by allowing its vital parameters (inertia weight and learning factors) to change within iterations. The method is evaluated using two real world examples in embedded applications and compared with existing covering methods.