Title: An adaptive reinforcement learning-based bat algorithm for structural design problems

Authors: Xian-Bing Meng; Han-Xiong Li; Xiao-Zhi Gao

Addresses: College of Mechanical and Electrical Engineering, Central South University, Changsha, China ' Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China ' School of Computing, University of Eastern Finland, Kuopio, Finland; Machine Vision and Pattern Recognition Laboratory, Lappeenranta University of Technology, Lappeenranta, Finland

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.

Keywords: bat algorithm; reinforcement learning; individual difference; adaptive tuning.

DOI: 10.1504/IJBIC.2019.101639

International Journal of Bio-Inspired Computation, 2019 Vol.14 No.2, pp.114 - 124

Received: 18 Aug 2017
Accepted: 13 Aug 2018
Published online: 19 Aug 2019 *

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