A Hybrid-coded Human Learning Optimization for mixed-variable optimization problems

Abstract

Human Learning Optimization (HLO) is an emerging meta-heuristic with promising potential, which is inspired by human learning mechanisms. Although binary algorithms like HLO can be directly applied to mixed-variable problems that contains both continuous values and discrete or Boolean values, the search efficiency and the performance of those algorithms may be significantly spoiled due to “the curse of dimensionality” caused by the binary coding strategy especially when the continuous parameters of problems require high accuracy. Therefore, this paper extends HLO and proposes a novel hybrid-coded HLO (HcHLO) framework to tackle mix-coded problems more efficiently and effectively, in which real-coded parameters are optimized by a new continuous HLO (CHLO) based on the linear learning mechanism of humans and the other variables are handled by the binary learning operators of HLO. Finally, HcHLO is adopted to solve 14 benchmark problems and its performance is compared with that of recent meta-heuristic algorithms. The experimental results show that the proposed HcHLO achieves the best-known overall performance so far on the test problems, which demonstrates the validity and superiority of HcHLO.