Artificial Bee Colony Algorithm Based on Adaptive Local Information Sharing Meets Multiple Dynamic Environments

[1]  Beatrice M. Ombuki-Berman,et al.  Dynamic vehicle routing using genetic algorithms , 2007, Applied Intelligence.

[2]  Xiaodong Li,et al.  A particle swarm model for tracking multiple peaks in a dynamic environment using speciation , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[3]  Bijaya Ketan Panigrahi,et al.  Adaptive particle swarm optimization approach for static and dynamic economic load dispatch , 2008 .

[4]  Hiroyuki Sato,et al.  Artificial Bee Colony Algorithm Based on Local Information Sharing in Dynamic Environment , 2015 .

[5]  Takeshi Nishida,et al.  Modification of ABC Algorithm for Adaptation to Time-Varying Functions , 2013 .

[6]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[7]  Changhe Li,et al.  A survey of swarm intelligence for dynamic optimization: Algorithms and applications , 2017, Swarm Evol. Comput..

[8]  Hiroyuki Sato,et al.  Toward robustness against environmental change speed by Artificial Bee Colony algorithm based on local information sharing , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[9]  Carlos Cruz,et al.  Optimization in dynamic environments: a survey on problems, methods and measures , 2011, Soft Comput..

[10]  Hui Cheng,et al.  Genetic Algorithms With Immigrants and Memory Schemes for Dynamic Shortest Path Routing Problems in Mobile Ad Hoc Networks , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).