An adaptive reinforcement learning-based bat algorithm for structural design problems. (26th July 2019)

• Record Type: Journal Article
• Authors: Meng, Xian-Bing; Li, Han-Xiong; Gao, Xiao-Zhi
• Is Part Of: International journal of bio-inspired computation. Volume 14:Number 2(2019)
• Page Start: 114
• Page End: 124
• Publication Date: 2019-07-26
• Subjects: bat algorithm -- reinforcement learning -- individual difference -- adaptive tuning; Biologically-inspired computing -- Periodicals; Computational biology -- Periodicals; 572.0285
• Languages: English
• ISSNs: 1758-0366

Abstract

A reinforcement learning-based bat algorithm is proposed for solving structural design problems. By incorporating reinforcement learning, the algorithm's performance feedback is formulated to adaptively select between algorithm's different operators. To improve the solution diversity, a new metric of individual difference is designed. The individual difference-based strategies are proposed to adaptively tune the algorithm's parameters. The variations of the pulse rates and loudness are newly designed to formulate their effects on the local search and foraging efficiency. Simulations and comparisons based on ten structural design problems with continuous/discrete variables demonstrate the superiority of the proposed algorithm.