A distributed hybrid heuristics of mean field annealing and genetic algorithm for load balancing problem

Abstract

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. © Springer-Verlag Berlin Heidelberg 2006.