A modified augmented Lagrangian with improved grey wolf optimization to constrained optimization problems

作者：Long, Wen; Liang, Ximing^*; Cai, Shaohong; Jiao, Jianjun; Zhang, Wenzhuan

来源：Neural Computing & Applications, 2017, 28(S1): S421-S438.

DOI：10.1007/s00521-016-2357-x

• Evolutionary computation-based algorithm
• Constrained optimization problems
• Augmented Lagrangian function
• Grey wolf optimization

This paper presents a novel constrained optimization algorithm named MAL-IGWO, which integrates the benefit of the improved grey wolf optimization (IGWO) capability for discovering the global optimum with the modified augmented Lagrangian (MAL) multiplier method to handle constraints. In the proposed MAL-IGWO algorithm, the MAL method effectively converts a constrained problem into an unconstrained problem and the IGWO algorithm is applied to deal with the unconstrained problem. This algorithm is tested on 24 well-known benchmark problems and 3 engineering applications, and compared with other state-of-the-art algorithms. Experimental results demonstrate that the proposed algorithm shows better performance in comparison to other approaches.

• 出版日期2017-12
• 单位北京建筑大学; 贵州财经大学