¹College of Computer, Hunan Science & Technology Economy Trade Vocation College, Hengyang, China
²College of Information Science and Engineering, Hunan University, Changsha, 410082, China
³Department of Mathematics and Computer Science, University of Chizhou, Chizhou, 247000, China
⁴College of Mathematics and Information Engineering, Jiaxing University, Jiaxing, 314001, China

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Abstract—To make glowworm swarm optimization (GSO) algorithm solve multi-extremum global optimization more effectively, taking into consideration the disadvantages and some unique advantages of GSO, the paper proposes a hybrid algorithm of Broyden–Fletcher–Goldfarb– Shanno (BFGS) algorithm and GSO, i.e., BFGS-GSO by adding BFGS local optimization operator in it, which can solve the problems effectively such as unsatisfying solving precision, and slow convergence speed in the later period. Through the simulation of eight standard test functions, the effectiveness of the algorithm is tested and improved. It proves that the improved BFGS-GSO abounds in better multi-extremum global optimization in comparison with the basic GSO.

Index Terms—Global optimization; GSO; BFGS operator; BFGS-GSO; function

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Cite: Aijia Ouyang, Libin Liu, Guangxue Yue, Xu Zhou, and Kenli Li, "BFGS-GSO for Global Optimization Problems," Journal of Computers vol. 9, no. 4, pp. 966-973, 2014.