A distributed hybrid algorithm for optimized resource allocation problem

Authors:
Kyeongmo Park;Sungcheol Kim;Chuleui Hong
Affiliations:
School of Computer Science and Information Engineering, The Catholic University, Korea;Software School, Sangmyung University, Seoul, Korea;Software School, Sangmyung University, Seoul, Korea
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

Citing 2
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Abstract

This paper presents a novel distributed Mean field Genetic algorithm called MGA for the load balancing problems in MPI environments. The proposed MGA is a hybrid algorithm of Mean Field Annealing (MFA) and Simulated annealing-like Genetic Algorithm (SGA). 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. It is also proved that the proposed distributed algorithm maintains the convergence properties of sequential algorithm while it achieves almost linear speedup as the problem size increases.