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|Title:||Multi-niche crowding in the development of parallel simulated annealing||Authors:||Wang, Z.-G.
Parallel genetic algorithm
|Issue Date:||2005||Citation:||Wang, Z.-G.,Rahman, M.,Wong, Y.-S. (2005). Multi-niche crowding in the development of parallel simulated annealing. GECCO 2005 - Genetic and Evolutionary Computation Conference : 1555-1556. ScholarBank@NUS Repository.||Abstract:||In this paper, a new hybrid of genetic algorithm (GA) and simulated annealing (SA), referred to as GSA, is presented. In this algorithm, SA is incorporated into GA to escape from the local optima. Then, the idea of hierarchical parallel G A is borrowed to parallelize GSA for the optimization of multimodal functions. In addition, multi-niche crowding is used to maintain the diversity in the population of parallel GSA. The performance of the proposed algorithms is evaluated against a standard set of multimodal benchmark functions. Multi-niche crowding PGSA and normal PGSA show some remarkable improvement in comparison with the conventional parallel GA and the breeder genetic algorithm.||Source Title:||GECCO 2005 - Genetic and Evolutionary Computation Conference||URI:||http://scholarbank.nus.edu.sg/handle/10635/73649||ISBN:||1595930108|
|Appears in Collections:||Staff Publications|
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