Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A self-generating fuzzy system with ant and particle swarm cooperative optimization
Expert Systems with Applications: An International Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Joint congestion control and processor allocation for task scheduling in grid over OBS networks
Expert Systems with Applications: An International Journal
Population-ACO for the automotive deployment problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Load balanced reliable task scheduling algorithm for heterogeneous systems
Journal of High Speed Networks
Hi-index | 12.05 |
A multi-objective task scheduling approach for grid over optical burst switching (GOBS) networks is proposed. It takes into account the selection of both computational resource and network resource and is able to simultaneously satisfy three objectives representing the requirements of grid users and resource providers, namely completion time, payment and load balancing. An ant colony optimization algorithm (ACO) for this multi-objective GOBS optimization problem is designed. The convergence and diversity preserving of the algorithm is compared with the nondominated sorting genetic algorithm (NSGA-II) through three performance metrics. Simulations are carried out to compare grid and network performance of these two algorithms. Grid scheduling performance comparison between single-objective optimization and multi-objective optimization based on ACO is also taken by simulations.