An Improved Slime Mould Algorithm for Demand Estimation of Urban Water Resources

A slime mould algorithm (SMA) is a new meta-heuristic algorithm, which can be widely used in practical engineering problems. In this paper, an improved (ESMA) proposed to estimate the water demand of Nanchang City. Firstly, opposition-based learning strategy and elite chaotic searching are improve SMA. By comparing ESMA with other intelligent optimization algorithms 23 benchmark test functions, it verified that has advantages fast convergence, high convergence precision, strong robustness. Secondly, based on data historical consumption local economic structure Nanchang, four estimation models, including linear, exponential, logarithmic, hybrid, established. The experiment takes City from 2004 2019 as example analyze, models optimized using determine model parameters, then tested. simulation results show all obtain better prediction accuracy, best effect hybrid model, accuracy up 97.705%. Finally, 2020–2024 forecasted.