Preface
Algorithm principle
Algorithm steps
①Initialization phase:
Similar to most intelligent algorithms, a batch of particles is randomly generated:
②Exploration stage
When snow or liquid water converted from snow is converted into steam, the search agent exhibits highly dispersed characteristics due to irregular movements. In this study, Brownian motion was used to simulate this situation. As a stochastic process, Brownian motion is widely used to simulate the foraging behavior of animals, the endless and irregular movement of particles, the fluctuation behavior of stock prices, etc. For standard Brownian motion, the step size is obtained by a probability density function based on a normal distribution with mean zero and variance one. The relevant mathematical expression is as follows:
Brownian motion is able to explore some potential regions in the search space. Therefore, it can well reflect the diffusion of vapor in the search space. The position calculation formula during the exploration process is as follows:
③ Mining stage
When snow is converted to liquid water through melting behavior, search agents are encouraged to develop high-quality solutions around the current best solution rather than expanding with highly dispersed features in the solution space. As one of the most classic snowmelt modelsÿ