Paper Title:
The Novel Compound Evolutionary Optimization Algorithm with Hybrid Discrete Variables and its Application to Mechanical Optimization
  Abstract

To overcome the problem of low convergence speed and sensitivity to local convergence with the traditional Artificial Fish-swarm Algorithm (AFSA) to handle complex functions, a novel compound evolutionary algorithm, called AFS-EMPCEOA, was introduced which is combined Artificial Fish-swarm Algorithm with the Elite Multi-parent Crossover Evolutionary Optimization Algorithm (EMPCEOA) that is GuoTao Algorithm improved by elite multi-parent crossover method. AFSEMPCEOA algorithm program with hybrid discrete variables was also developed. The computing example of mechanical optimization design shows that this algorithm has no special requirements on the characteristics of optimal designing problems, which has a fairly good universal adaptability and a reliable operation of program with a strong ability of overall convergence and high efficiency.

  Info
Periodical
Advanced Materials Research (Volumes 97-101)
Edited by
Zhengyi Jiang and Chunliang Zhang
Pages
3276-3280
DOI
10.4028/www.scientific.net/AMR.97-101.3276
Citation
Y. X. Luo, "The Novel Compound Evolutionary Optimization Algorithm with Hybrid Discrete Variables and its Application to Mechanical Optimization", Advanced Materials Research, Vols. 97-101, pp. 3276-3280, 2010
Online since
March 2010
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