A novel human learning optimization algorithm with Bayesian inference learning

Humans perform Bayesian inference in a wide variety of tasks, which can help people make selection decisions effectively and therefore enhances learning efficiency and accuracy. Inspired by this fact, this paper presents a novel human learning optimization algorithm with Bayesian inference learning (HLOBIL), in which a Bayesian inference learning operator (BILO) is developed to utilize the inference strategy for enhancing learning efficiency. The in-depth analysis shows that the proposed BILO can efficiently improve the exploitation ability of the algorithm as it can achieve the optimal values and retrieve the optimal information with the accumulated search information. Besides, the exploration ability of HLOBIL is also strengthened by the inborn characteristics of Bayesian inference. The experimental results demonstrate that the developed HLOBIL is superior to previous HLO variants and other state-of-art algorithms with its improved exploitation and exploration abilities. (c) 2023 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces a novel human learning optimization algorithm called HLOBIL, which utilizes Bayesian inference learning strategy for enhanced learning efficiency. The proposed Bayesian inference learning operator (BILO) improves the exploitation ability by achieving optimal values and retrieving optimal information. Additionally, the inborn characteristics of Bayesian inference enhance the exploration ability of HLOBIL. Experimental results demonstrate the superiority of HLOBIL over previous HLO variants and other state-of-art algorithms in terms of exploitation and exploration abilities.