An improved Henry gas solubility optimization for optimization tasks

摘要

The henry gas solubility optimization (HGSO) is a new nature-inspired algorithm that mimics Henry Gas Solubility to solve global optimization problems. The main changes of premature convergence and poor balance between exploration and exploitation persist, which cannot yet do well in solving some complex optimization problems. To solve the above problems and get better performance, and improved henry gas solubility optimization with dynamic opposite learning, sine cosine factor, conversion probability and interval contraction strategy is proposed in this paper. Firstly, to increase population diversity, using the asymmetry of the dynamic-opposite learning search space to enable individuals to traverse the entire solution space as much as possible. Secondly, change the position update method of henry gas solubility optimization and combine the sine and cosine strategies to better balance the exploration and exploitation of the algorithm. Thirdly, the interval shrinking strategy makes the algorithm better approach the optimal solution and accelerates the algorithm convergence. Finally, the well-known CEC2017 benchmark functions and three real-world engineering design problems were employed to demonstrate the performance of our algorithm. The diversity of algorithms and the coordination of different strategies are analyzed. The experimental results and statistical analyses show that the performance of our algorithm is better than the comparison algorithms.