An Improved Adaptive Human Learning Optimization Algorithm with Reasoning Learning

Abstract

Human Learning Optimization (HLO) is a simple yet highly efficient metaheuristic developed based on a simplified human learning model. To further extend the research of HLO, the social reasoning learning operator (SRLO) is introduced. However, the learning ability of social imitating learning operator (SILO) and SRLO is constant in the process of iterations, which is not true in a real human population as humans often adopt dynamic learning strategies to solve the problem. Inspired by this fact, an improved adaptive human learning optimization algorithm with reasoning learning (AHLORL) is proposed to enhance the global search ability, in which an adaptive ps strategy is carefully designed to sufficiently motivate the roles of SILO and SRLO and dynamically adjust the learning efficiency of the algorithm at different stages of iterations. Then, a comprehensive parameter study is performed to explain why the proposed adaptive strategy can exploit the optimization ability of SILO and SRLO effectively. Finally, the AHLORL is applied to solve the CEC 15 benchmark functions as well as multidimensional knapsack problems (MKPs), and its performance is compared with the previous HLO variants as well as the other recent metaheuristics. The experimental results show that the proposed AHLORL outperforms the other algorithms in terms of search accuracy and scalability.