Hybrid Henry Gas Solubility Optimization Algorithm Based on the Harris Hawk Optimization

Abstract

Henry Gas Solubility Optimization Algorithm (HGSO) is a physical-based algorithm based on Henry's Law and simulates the process that the solubility of gas in liquid changes with temperature. The search strategy of the HGSO is very simple, which results in the algorithm having poor exploitation ability and unable to find a more accurate optimal solution. The Harris Hawk optimization algorithm (HHO) has strong exploitation because of its diverse exploitation strategies. In order to make up for the shortcomings of HGSO algorithm, this article proposes an improved Henry gas solubility optimization algorithm (HHO-HGSO) based on the Harris Hawk optimization. During the iteration, taking the escaping energy function of the HHO algorithm as an indicator and determining a threshold, when the escaping energy function value is greater than the threshold, the algorithm conducts the search strategy of the HGSO algorithm, when less than the threshold, executes four exploitation strategies of Harris Hawk. In order to verify the performance of the proposed algorithm, HHO-HGSO is used to optimize CEC2005 and CEC2017 benchmark test functions and solve 4 real engineering design problems. Marine predators algorithm (MPA), Whale optimization algorithm (WOA), Lightning search algorithm (LSA), Water cycle algorithm (WCA), HGSO, and HHO were used to conduct comparative experiments. The simulation results show that the proposed HHO-HGSO algorithm has strong optimization ability. The core code of the paper has been uploaded in https://github.com/xwaiyy123/HHO-HGSO.

Keywords

• Henry gas solubility optimization algorithm
• Harris Hawk predation strategy
• exploration
• exploitation