PARALLEL LOCAL SEARCH TO IMPROVE THE PERFORMANCE OF GENETIC ALGORITHMS

Youzhi Zheng, Zheng Qin and Junliang Chen
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Youzhi Zheng: Department of Computer Science and Technology, Tsinghua University, Beijing, China
Zheng Qin: Department of Computer Science and Technology, Tsinghua University, Beijing, China
Junliang Chen: School of Software, Tsinghua University, Beijing, China

Chapter 5 in Challenges in Information Technology Management, 2008, pp 31-36 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractGenetic algorithms are promising search techniques dealing with the combinatorial optimization problems. The main disadvantage of GA is the slow convergence rate of the search. In order to improve convergence rate of the search, the former researchers put forward two common strategies, one is local search methods and the other is parallelization. This paper tries to combine the two strategies and design the architecture of parallel hybrid GA. We propose three parallel hybrid GAS: (1) GA holds the shared population, (2) local search holds the shared population, and (3) independent shared population. This paper selects message passing interface (MPI) to implement the parallel hybrid GAS program. We compare our parallel hybrid GA with the pure GA and the pipelining hybrid GA, and the results of the simulation show that parallel hybrid GAS can combine the merits of the local search approach and the parallel mechanism.

Keywords: Information Technology; Knowledge Management; Computing (search for similar items in EconPapers)
Date: 2008
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