Adaptive search based Grey Wolf optimization algorithm for multi-objective optimization of ethylene cracking furnace

Abstract

The ethylene cracking furnace (ECF) is an important device for producing ethylene and propylene, so the optimization problem of the ECF is crucial. However, traditional optimization algorithms such as the grey wolf optimization (GWO) algorithm, are prone to getting stuck in local optima under the early stages and have low optimization accuracy under the later stage, which cannot effectively optimize the production of the ECF. Therefore, a novel multi-objective grey wolf optimization algorithm based on the adaptive search (ASMOGWO) is proposed. The non-linear convergence factor of the cosine transform in the ASMOGWO algorithm offsets its discovery and development capabilities. Then, the velocity formula of the GWO is updated based on the velocity update, effectively preventing individuals from entering local optima and improving the convergence performance. Meanwhile, the linearly decreasing inertia weight coefficients is proposed to control the convergence speed of the ASMOGWO. Compared with other optimization algorithms through public experiments, the ASMOGWO has good effects. Finally, the ASMOGWO algorithm is applied to optimize the ethylene yield and the propylene yield of the ECF. The result shows the proposed ASMOGWO has better feasibility than the original GWO algorithm and other optimization algorithms. Meanwhile, the optimized ethylene yield increased by 1.3570 %, while the propylene yield decreased by 0.0093 %.