A new artificial bee algorithm and its application into image registration

As a popular optimization algorithm, artificial bee colony algorithm (ABC) has attracted many attention in recent years. In this paper, for accelerating its convergence rate, a new concept of learning point is present, which is composed by some individuals with better performance. The performance of the proposed algorithm is verified by the traditional benchmark function and an image registration problem.

[1]  Ahmad Ayatollahi,et al.  A new hybrid particle swarm optimization for multimodal brain image registration , 2012 .

[2]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[3]  Sam Kwong,et al.  Gbest-guided artificial bee colony algorithm for numerical function optimization , 2010, Appl. Math. Comput..

[4]  Qian Zhang,et al.  Image registration with position and similarity constraints based on genetic algorithm , 2014, 2014 10th International Conference on Natural Computation (ICNC).

[5]  Jacek M. Zurada,et al.  An approach to multimodal biomedical image registration utilizing particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[6]  Ivanoe De Falco,et al.  Differential Evolution as a viable tool for satellite image registration , 2008, Appl. Soft Comput..

[7]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[8]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[9]  Xianneng Li,et al.  Search experience-based search adaptation in artificial bee colony algorithm , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[10]  Yu Xue,et al.  A self-adaptive artificial bee colony algorithm based on global best for global optimization , 2017, Soft Computing.