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Mechanism and Convergence of Bee-Swarm Genetic Algorithm

  • Di Wu
  • Rongyi Cui
  • Changrong Li
  • Guangjun Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)

Abstract

Bee-Swarm genetic algorithm based on reproducing of swarm is a novel improved genetic algorithm. Comparing to GA, there are two populations, one for global search, and another for local search. Only best one can crossover. The genetic operators include order crossover operator, adaptive mutation operator and restrain operator. The simulated annealing is also introduced to help local optimization. The method sufficiently takes the advantage of genetic algorithm such as group search and global convergence, and quick parallel search can efficiently overcome the problems of local optimization. Theoretically, the capability of finding the global optimum is proved, and a necessary and sufficient condition is obtained namely. The convergence and effective of BSGA is proved by Markov chain and genetic mechanism. Finally, several testing experiments show that the Bee-Swarm genetic algorithm is good.

Keywords

absolute mating adaptive crossover simulated annealing algorithm effective 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Di Wu
    • 1
  • Rongyi Cui
    • 2
  • Changrong Li
    • 1
  • Guangjun Song
    • 1
  1. 1.College of Computer and Control EngineeringQiqihaer UniversityQiqihaer HeilongjiangChina
  2. 2.College of Computer EngineeringYanbian UniversityYanji JilinChina

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