Mechanism and convergence of bee-swarm genetic algorithm

Authors:
Di Wu;Rongyi Cui;Changrong Li;Guangjun Song
Affiliations:
College of Computer and Control Engineering, Qiqihaer University, Qiqihaer Heilongjiang, China;College of Computer Engineering, Yanbian University, Yanji Jilin, China;College of Computer and Control Engineering, Qiqihaer University, Qiqihaer Heilongjiang, China;College of Computer and Control Engineering, Qiqihaer University, Qiqihaer Heilongjiang, China
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2010

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Genetic algorithms with sharing for multimodal function optimization

Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application

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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.