Multi-Objective Optimization of Wastewater Treatment Process Based on Improved Fast Non-Dominated Sorting Algorithm with Hybrid Crossover Operator

With the increasingly serious water pollution problems, wastewater treatment process has become a hot spot in today's research. The key issue of wastewater treatment process is to achieve the optimization of the system's energy consumption and cost reduction under the premise of meeting the effluent water quality standards, so as to achieve the purpose of “maintaining quality and reducing consumption”. The traditional fast non-dominated sequential genetic (NSGA II) algorithm uses Simulated Binary Crossover (SBX) to generate offspring to deal with the optimization problem, which may have the problems of limited search range and unstable evolutionary process, etc. In this paper, we propose a hybrid crossover algorithm based on SBX to generate offspring. In this paper, an improved fast non-dominated sorting algorithm based on hybrid crossover operator (MIX-NSGA II) is proposed to solve the above problems, and by introducing Normal Distribution Crossover (NDX) in the crossover process, MIX-NSGA II is proposed to strengthen the global search capability, improve the convergence speed and algorithmic running speed, and obtain higher quality optimized solutions.