TY - GEN
T1 - Random grouping brain storm optimization algorithm with a new dynamically changing step size
AU - Cao, Zijian
AU - Shi, Yuhui
AU - Rong, Xiaofeng
AU - Liu, Baolong
AU - Du, Zhiqiang
AU - Yang, Bo
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Finding the global optima of a complex real-world problem has become much more challenging task for evolutionary computation and swarm intelligence. Brain storm optimization (BSO) is a swarm intelligence algorithm inspired by human being’s behavior of brainstorming for solving global optimization problems. In this paper, we propose a Random Grouping BSO algorithm termed RGBSO by improving the creating operation of the original BSO. To reduce the load of parameter settings and balance exploration and exploitation at different searching generations, the proposed RGBSO adopts a new dynamic step-size parameter control strategy in the idea generation step. Moreover, to decrease the time complexity of the original BSO algorithm, the improved RGBSO replaces the clustering method with a random grouping strategy. To examine the effectiveness of the proposed algorithm, it is tested on 14 benchmark functions of CEC2005. Experimental results show that RGBSO is an effective method to optimize complex shifted and rotated functions, and performs significantly better than the original BSO algorithm.
AB - Finding the global optima of a complex real-world problem has become much more challenging task for evolutionary computation and swarm intelligence. Brain storm optimization (BSO) is a swarm intelligence algorithm inspired by human being’s behavior of brainstorming for solving global optimization problems. In this paper, we propose a Random Grouping BSO algorithm termed RGBSO by improving the creating operation of the original BSO. To reduce the load of parameter settings and balance exploration and exploitation at different searching generations, the proposed RGBSO adopts a new dynamic step-size parameter control strategy in the idea generation step. Moreover, to decrease the time complexity of the original BSO algorithm, the improved RGBSO replaces the clustering method with a random grouping strategy. To examine the effectiveness of the proposed algorithm, it is tested on 14 benchmark functions of CEC2005. Experimental results show that RGBSO is an effective method to optimize complex shifted and rotated functions, and performs significantly better than the original BSO algorithm.
KW - Brain storm optimization
KW - Dynamic step size
KW - Random grouping
UR - https://www.scopus.com/pages/publications/84947813856
U2 - 10.1007/978-3-319-20466-6_38
DO - 10.1007/978-3-319-20466-6_38
M3 - Conference Proceeding
AN - SCOPUS:84947813856
SN - 9783319204659
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 357
EP - 364
BT - Advances in Swarm and Computational Intelligence - 6th International Conference, ICSI 2015 held in conjunction with the 2nd BRICS Congress, CCI 2015, Proceedings
A2 - Gelbukh, Alexander
A2 - Tan, Ying
A2 - Das, Swagatam
A2 - Engelbrecht, Andries
A2 - Buarque, Fernando
A2 - Shi, Yuhui
PB - Springer Verlag
T2 - 6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015
Y2 - 25 June 2015 through 28 June 2015
ER -