An adaptive human learning optimization with enhanced exploration–exploitation balance

Annals of Mathematics and Artificial Intelligence. 2023. Vol. 97. P. 117–216.

Du J., Wen Y., Wang L., Zhang P., Fei M., Pardalos P. M.

Human Learning Optimization (HLO) is a simple yet efficient binary meta-heuristic, in
which three learning operators, i.e. the random learning operator (RLO), individual
learning operator (ILO) and social learning operator (SLO), are developed to mimic
human learning mechanisms to solve optimization problems. Among these three operators,
RLO directly influences the exploration and exploitation abilities of HLO, and
therefore its control parameter pr is of great importance since it controls the balance
between exploration and exploitation. In this paper, an adaptive human learning optimization
with enhanced exploration-exploitation balance (AHLOee) is proposed to improve
the performance of HLO, in which a new adaptive pr strategy is carefully designed to
meet the different requirements of HLO at different stages of iterations. A comprehensive
parameter study is performed to evaluate the influences of the proposed adaptive strategy
on exploration and exploitation, and then the deep insights on the role of RLO and the
reason why the proposed adaptive strategy can achieve a practically ideal trade-off
between exploration and exploitation are provided. The experimental results on the
CEC05 and CEC15 benchmarks demonstrate that the proposed AHLOee has advantages
over previous HLO variants and outperforms recent state-of-art binary meta-heuristics.

Keywords: meta-heuristics, social learning, Human learning optimization, Adaptive HLO, Random learning

Publication based on the results of: Modern approaches to analysis of network structures (2022)