An improved adaptive Genetic Algorithm in Optimization of Partner Selection

Author

Ma, Xuesen ; Han, Jianghong ; Wei, Zhenchun ; Wang, Yuefei

Author_Institution

Hefei Univ. of Technol., Hefei

Volume

3

fYear

2007

fDate

July 30 2007-Aug. 1 2007

Firstpage

455

Lastpage

460

Abstract

Partner selection is a critical problem in organizing virtual enterprises according to 3 main indexes of cost, credit degree and make span provided by the candidates. An improved genetic algorithm (AGA) with total fitness ranking-based selection and adaptive operator is presented. Selection based on total fitness ranking make multi-objective problem several single- objective optimizations, insures rational interval between individuals and avoids premature convergence. Crossover and mutation operator are adjusted according to the fitness and iterative degree. Thus, each individual owns the ability of self-adaptation with the variation of environment. The simulated results verified the effectiveness of AGA.

Keywords

costing; genetic algorithms; iterative methods; mathematical operators; virtual enterprises; adaptive genetic algorithm; adaptive operator; cost index; credit degree index; crossover operator; iterative degree; make span index; multiobjective problem; mutation operator; partner selection optimization; single-objective optimizations; total fitness ranking-based selection; virtual enterprises; Algorithm design and analysis; Analytical models; Convergence; Data envelopment analysis; Distributed computing; Educational technology; Fuzzy neural networks; Genetic algorithms; Genetic mutations; Optimization methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on

Conference_Location

Qingdao

Print_ISBN

978-0-7695-2909-7

Type

conf

DOI

10.1109/SNPD.2007.191

Filename

4287896