Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a dynamic, non-dominated sorting, multi-objective particle-swarm-based optimiser, named hierarchical non-dominated sorting particle swarm optimiser (H-NSPSO), for memory usage optimisation in embedded systems. It significantly reduces the computational complexity of others multi- objective particle swarm optimisation (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 neighbourhood topology. H-NSPSO has been made adaptive in nature by allowing its vital parameters (inertia ight 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.
['José L. Risco-Martín', 'Oscar Garnica', 'Juan Lanchares', 'J. Ignacio Hidalgo', 'David Atienza']
I am writing a research paper on a idea Particle swarm optimisation of memory usage in embedded systems
[{"from": "human", "value": "I am writing a research paper on a idea Particle swarm optimisation of memory usage in embedded systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a dynamic, non-dominated sorting, multi-objective particle-swarm-based optimiser, named hierarchical non-dominated sorting particle swarm optimiser (H-NSPSO), for memory usage optimisation in embedded systems. It significantly reduces the computational complexity of others multi- objective particle swarm optimisation (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 neighbourhood topology. H-NSPSO has been made adaptive in nature by allowing its vital parameters (inertia ight 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."}]