Abstract
In order to improve the optimized performance of bee genetic algorithm, this paper proposed the evolution strategy by introducing immune evolution and chaotic mutation into bee genetic algorithm. This algorithm carries out the chaotic mutation to the some individuals with the lower fitness values, meanwhile the crossover and mutation operations were conducted between the some individuals with the higher fitness values and the optimal individual (queen) in population. In addition, the optimal individual in each generation should make iterative calculation by immune evolution. Therefore, as the iterations go on, this algorithm not only converges faster, but also close to the global optimal solution with higher precision.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
