Satellite Module Layout Design Based on Adaptive Bee Evolutionary Genetic Algorithm

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The main problems for Genetic Algorithm (GA) to deal with the complex layout design of satellite module lie in easily trapping into local optimality and large amount of consuming time. To solve these problems, the Bee Evolutionary Genetic Algorithm (BEGA) and the adaptive genetic algorithm (AGA) are introduced. The crossover operation of BEGA algorithm effectively reinforces the information exploitation of the genetic algorithm, and introducing random individuals in BEGA enhance the exploration capability and avoid the premature convergence of BEGA. These two features enable to accelerate the evolution of the algorithm and maintain excellent solutions. At the same time, AGA is adopted to improve the crossover and mutation probability, which enhances the escaping capability from local optimal solution. Finally, satellite module layout design based on Adaptive Bee Evolutionary Genetic Algorithm (ABEGA) is proposed. Numerical experiments of the satellite module layout optimization show that: ABEGA outperforms SGA and AGA in terms of the overall layout scheme, enveloping circle radius, the moment of inertia and success rate.

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193-197

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April 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] FMJ De Bont, EHL Aarts, P Meehan, et al. Placement of shapeable blocks. Philips Journal of Research, 1988, 43(1): 1-27.

Google Scholar

[2] TANG Fei, TENG Hong-fei. A Modified Genetic Algorithm and Its Application to Layout Optimization. Journal of Software (in Chinese), 1999, 10(10): 1096-1102.

Google Scholar

[3] ZHANG Bao, Partiele Swarm Optimization Algorithm for Satellite Module Layout Optimization, PhD dissertation(in Chinese), Dalian University of Technology, (2007).

Google Scholar

[4] J. Z. HUO, G LI, Q., H. F. TENG. Humance-Computer Cooperative Ant Colony/Genetic Algorithm for Satellite Module Layout Desigen. Chinese Journal of Mechanical Engineeral(in Chinese), 2005, 41(003): 112-116.

DOI: 10.3901/jme.2005.03.112

Google Scholar

[5] J.H. Holland. Adaptation in natural and artificial systems, University of Michigan press. Ann Arbor, MI, 1975, 1(97): 5.

Google Scholar

[6] MA Li-Xiao, WANG Jiang-qing. Application of Genetic Algorithms in Solving the Optimal Combination Problem. COMPUTER ENGINEERING & SCIENCE (in Chinese), 2005, 27(7): 72-73.

Google Scholar

[7] Shang Fei, Research Application of the Genetic Algorithm in Image Processing, PhD dissertation(in Chinese), North China Electric Power University (Beijing), (2007).

Google Scholar

[8] MENG Wei, HAN Xue-dong, HONG Bing -rong. Bee Evolutionary Genetic Algorithm. Acta electronica Sinica (in Chinese), 2006, 34(7): 1294-1300.

Google Scholar

[9] M Srinivas, Lalit M. Patnaik. Adaptive probabilities of crossover and mutation in genetic algorithms. Systems, Man and Cybernetics, IEEE Transactions on, 1994, 24(4): 656-667.

DOI: 10.1109/21.286385

Google Scholar

[10] Kalyanmoy Deb, Samir Agrawal. Understanding interactions among genetic algorithm parameters. Foundations of Genetic Algorithms, 1999: 265-286.

Google Scholar

[11] Günter Rudolph. Convergence analysis of canonical genetic algorithms. Neural Networks, IEEE Transactions on, 1994, 5(1): 96-101.

DOI: 10.1109/72.265964

Google Scholar