Research on Multi-Level Scheduling of Mine Water Reuse Based on Improved Whale Optimization Algorithm

Abstract

Aiming at the problem of the inefficiency of coal mine water reuse, a multi-level scheduling method for mine water reuse based on an improved whale optimization algorithm is proposed. Firstly, the optimization objects of mine water reuse time and reuse cost are used to establish the optimal scheduling model of mine water. Secondly, in order to overcome the defect that the whale optimization algorithm (WOA) is prone to local convergence, the opposition-based learning strategy is introduced to speed up the convergence speed, the Levy flight strategy is used to enhance the ability of the algorithm to jump out of the local optimization, the nonlinear convergence factor is used to balance the global and local search ability, and the adaptive inertia weight is used to improve the optimization accuracy of the algorithm. Finally, the improved whale optimization algorithm (IWOA) is applied to the mine water optimization scheduling model with multiple objects and constraints. The results show that the reuse efficiency of the multi-level scheduling method of mine water reuse is increased by 30.2% and 31.9%, respectively, in the heating and nonheating seasons, which can significantly improve the reuse efficiency of mine water and realize the efficient utilization of mine water reuse deployment. At the same time, experiments show that the improved whale optimization algorithm has higher convergence accuracy and speed, which proves the feasibility and superiority of its improvement strategies.

A Nonlinear Adaptive Weight-Based Mutated Whale Optimization Algorithm and Its Application for Solving Engineering Problems

“…Lei et al proposed an improved WOA (IWOA) that embeds Levy flight, opposition-based learning, and nonlinear convergence factor to optimize the mine water optimization scheduling model. The results show that the reuse efficiency of the multi-level scheduling method of mine water reuse is increased by 30.2% and 31.9%, respectively [51]. Jiao et al mutated WOA by introducing the crazy operator and the golden sine algorithm, which improved the global search ability of the WOA, and its outstanding performance was also verified in simulation results [52].…”

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