A Hybrid Multi-gene Genetic Programming with Capuchin Search Algorithm for Modeling a Nonlinear Challenge Problem: Modeling Industrial Winding Process, Case Study

摘要

Motivated by the increasing complexity and operational productivity of industrial processes, the need for efficient modeling schemes for industrial systems is highly demanded. This study presents a new simulator model for a real winding process based on a combination of Multi-gene Genetic Programming (MGP) and Capuchin Search Algorithm (CapSA), referred to as MGP-CapSA modeling approach. CapSA is a meta-heuristic algorithm used to optimize the coefficients of the regression equations of the MGP technique. The winding process has tensions in the web between reels 1 and 2 and between reels 2 and 3. On this basis, two mathematical models were developed by the MGP-CapSA method to estimate the tensions in the web for this process. The efficacy and superiority of the proposed MGP-CapSA method were verified by extensive experiments and hypothesis testing, and the proposed method was then compared with other well-known intelligent and conventional modeling methods. The proposed MGP-CapSA method can be exploited to enhance control performance and achieve robust fault-tolerant system. A comparison of the MGP-CapSA method with other promising modeling methods corroborates the performance level of MGP-CapSA over those competitors. The results demonstrate that MGP-CapSA is a suitable method for generating robust models for complex nonlinear systems.