Observations on Using Genetic Algorithms for Dynamic Load-Balancing
IEEE Transactions on Parallel and Distributed Systems
Train Traffic Deviation Handling Using Tabu Search and Simulated Annealing
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 3 - Volume 03
Multi-niche crowding in the development of parallel genetic simulated annealing
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Improving Load Balance with Flexibly Assignable Tasks
IEEE Transactions on Parallel and Distributed Systems
Hybrid Genetic Algorithm and Simulated Annealing (HGASA) in Global Function Optimization
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Research on Multi-constrained QoS Routing Scheme Using Mean Field Annealing
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
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In this paper, we introduce a novel distributed Mean field Genetic algorithm called MGA for the resource allocation problems in MPI environments, which is an important issue in parallel processing. The proposed MGA is a hybrid algorithm of Mean Field Annealing (MFA) and Simulated annealing-like Genetic Algorithm (SGA). SGA uses the Metropolis criteria for state transition as in simulated annealing to keep the convergence property in MFA. The proposed MGA combines the benefit of rapid convergence property of MFA and the effective genetic operations of SGA. Our experimental results indicate that the composition of heuristic mapping methods improves the performance over the conventional ones in terms of communication cost, load imbalance and maximum execution time.