Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network

The advent of new data-mining techniques and, more recently, swarm-based optimization algorithms have antiquated traditional models in the field of energy performance analysis. This paper investigates the potential of two state-of-the-art hybrid methods, namely grasshopper optimization algorithm (GOA) and gray wolf optimization (GWO) in improving the neural assessment of heating load (HL) of residential buildings. To achieve this goal, eight HL influential factors including glazing area distribution, relative compactness, overall height, surface area, roof area, wall area, orientation, and glazing area are considered for preparing the required dataset. A population-based sensitivity analysis is then carried out to use the best-fitted structures of each ensemble. The results showed that utilizing both GOA and GWO algorithms results in increasing the accuracy of the neural network. From comparison viewpoint, it was found that the GWO (error = 2.2899 and correlation = 0.9551) surpasses GOA (error = 2.4459 and correlation = 0.9486) in adjusting the computational parameters of the proposed neural system.

Automated summary

This paper explores the potential of the grasshopper optimization algorithm (GOA) and gray wolf optimization (GWO) in enhancing the accuracy of neural assessment of heating load (HL) for residential buildings. The study found that GWO outperforms GOA in adjusting the computational parameters of the proposed neural system.