Practical Data Structures Using C/C++ with 3.5 Disk
Practical Data Structures Using C/C++ with 3.5 Disk
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Storage Management Programmable Process
Storage Management Programmable Process
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
SCOPES '07 Proceedingsof the 10th international workshop on Software & compilers for embedded systems
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Memory-access-aware data structure transformations for embedded software with dynamic data accesses
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special section on the 2002 international symposium on low-power electronics and design (ISLPED)
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
A hierarchical particle swarm optimizer and its adaptive variant
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hardware/software co-design for particle swarm optimization algorithm
Information Sciences: an International Journal
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In this paper, we 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(mN2) 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 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.