Brain Storm Optimization Algorithm Based on Improved Clustering Approach Using Orthogonal Experimental Design

Wang, Rui1; Ma, Lianbo1; Zhang, Tao1; Cheng, Shi2; Shi, Yuhui3

10.1109/CEC.2019.8790307

2019

978-1-7281-2154-3

2019 IEEE Congress on Evolutionary Computation, CEC 2019

262-270

10-13 June 2019

Wellington, New zealand

345 E 47TH ST, NEW YORK, NY 10017 USA

Institute of Electrical and Electronics Engineers Inc.

The brain storm optimization (BSO) algorithm is a new and promising swarm intellgience paradigm, inspired from the behaviors of the human process of brainstorming. The noverty of BSO lies in the clustering mechanism where the ideas are clustered into a set of groups and each idea learns from experiences of one inter-cluster or two intra-cluster neighbors. However, this mechanism is inefficient to deal with complex optimiaiton problems. In this paper, we propose an improved BSO algorithm called OSBSO using orthogonal experimental design (OED) strategy, which aims to discover useful search experiences for improving the convergence and solution accurancy. In OSBSO, two new clustering procedures are developed, i.e., orthogonal initialization and orthogonal clustering. The orthogonal initialization aims to improve the uniformity of the initial cluster centers in the objective space instead of the decision space, which can enhance the convergence performance. The orthogonal clustering uses the information between inter- cluster and intra-cluster indviduals to alleviate the evolution stagnation of clusters. Experiments are conducted on a set of the CEC2017 benchmark functions and the results verify the effectivenss and efficiency of OSBSO.
© 2019 IEEE.

BSO Orthogonal experimental design Swarm intelligence

其他

英语

EI ; CPCI-S

National Natural Science Foundation of China[61503373]

Engineering ; Mathematical & Computational Biology

Engineering, Electrical & Electronic ; Mathematical & Computational Biology

WOS:000502087100036

20193507374121

Behavioral research ; Cluster analysis ; Evolutionary algorithms ; Statistics ; Storms ; Swarm intelligence

Precipitation:443.3 ; Computer Software, Data Handling and Applications:723 ; Information Sources and Analysis:903.1 ; Mathematical Statistics:922.2 ; Social Sciences:971

EV Compendex

1.Software College Northeastern University, Shenyang, China
2.School of Computer Science, Shaanxi Normal University, Xi'an, China
3.Computer Science and Engineering College, Southern University of Science and Technology, Shenzhen, China

Wang, Rui,Ma, Lianbo,Zhang, Tao,et al. Brain Storm Optimization Algorithm Based on Improved Clustering Approach Using Orthogonal Experimental Design[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2019:262-270.