Equipment Selection Optimization of Oil Well Distribution Network by An Improved Multi-objective Slime Mold Algorithm

In oilfield production, the parameters of supporting equipment are crucial in determining the efficiency of oil wells. To achieve the goal of energy conservation, it is important to manage these parameters effectively. This paper establishes a multi-objective optimization model for equipment selection in the oil well distribution network, synthetically considering energy consumption, operating cost, and system efficiency. An improved multi-objective slime mold optimization algorithm (AMOSMA) is proposed to solve the model. The algorithm introduces an opposition-based learning strategy of convex lens imaging principle and Cauchy mutation strategy to improve population diversity and convergence accuracy. The simulation experiment demonstrates the use of a multi-objective optimization model to reduce energy consumption and operating costs by optimizing pumping parameters.