Study of improved hierarchy genetic algorithm based on adaptive niches

  • Authors:

  • Qiao-Ling Ji;Wei-Min Qi;Wei-You Cai;Yuan-Chu Cheng;Feng Pan

  • Affiliations:

  • College of Power and Mechanical engineering, Wuhan University, Wuhan, China;College of Power and Mechanical engineering, Wuhan University, Wuhan, China;College of Power and Mechanical engineering, Wuhan University, Wuhan, China;College of Power and Mechanical engineering, Wuhan University, Wuhan, China;Three Gorges Hydropower Plant, Yichang, China

  • Venue:
  • ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
  • Year:
  • 2005

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Abstract

Canonical genetic algorithms have the defects of pre-maturity and stagnation when applied in optimizing problems. In order to avoid the shortcomings, an adaptive niche hierarchy genetic algorithm (ANHGA) is proposed. The algorithm is based on the adaptive mutation operator and crossover operator to adjust the crossover rate and probability of mutation of each individual, whose mutation values are decided using individual gradient. This approach is applied in Percy and Shubert function optimization. Comparisons of niche genetic algorithm (NGA), hierarchy genetic algorithm (HGA) and ANHGA have been done by establishing a simulation model and the results of mathematics model and actual industrial model show that ANHGA is feasible and efficient in the design of multi-extremum.