In the oil and gas business, porous and fracture imaging is being employed widely to forecast the amount of petroleum present under suitable trap circumstances. These characteristics have many uses in the transportation of contaminants, the storage of radioactive waste in bedrock and the storage of carbon dioxide. In the recent literature, there are a few techniques presented that are able to automatically detect pores and cracks from images. Support Vector Machines (SVMs) are used as a classification tool by many researchers, while deep learning systems are used by a small number of them. SVMs are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis; deep (supervised, semi-supervised or unsupervised) learning is based on artificial neural networks with representation learning (the term \"deep\" refers to the use of multiple layers in the network). The reported accuracy of the latter, however, is unsatisfactory due to several reasons. Despite its limitations, as discussed in [2], the multi-objective method suggested by the authors outperformed the most popular deep learning system approaches in terms of classification accuracy. In the third paper [3] of this Special Issue, a code called MolecGeom is provided. It is based on algorithms for stepwise distortions of bond angles and dihedral angles of polyatomic molecules. The energy for each molecule shape is used to construct Potential Energy Surfaces (PESs). To compute PESs, the Born-Oppenheimer approximation is used, which assumes that the atomic nuclei within a molecule are stationary with respect to the motion of its electrons. Applications involving PESs focus on how nuclear mobility affects molecular characteristics. This involves calculating reaction rates, forecasting vibrational spectra and identifying equilibrium geometries corresponding to stationary and saddle point energies. This multi-objective study focuses on the creation of a new technique for computing PESs and the investigation of the components of molecular geometry in terms of incremental change that provides thorough sampling while maintaining the accuracy of PES points. MolecGeom is used to create geometric data when calculating PESs for theoretical simulations of the vibrational-rotational spectra of water and formaldehyde. The fourth paper [4] of this Special Issue proposes a multi-objective mathematical model and a Pareto analysis to determine the coefficients of an equation describing the thermal conductivity of refrigerants with a low global warming potential. The Non-dominated Sorting Genetic Algorithm II (NSGAII) was utilized for the first time to define a thermophysical characteristic like thermal conductivity. NSGA-II is a modified variant of NSGA that addresses a variety of constrained optimization problems. In contrast to the NSGA, which has a complexity of \(mn^{3}\) (where \(m\) is the number of objectives and \(n\) is the population size), the NSGA-II is faster (complexity \(mn^{2}\)), has a better sorting algorithm, includes elitism to prevent an already-found Pareto optimal solution from being deleted, and uses an explicit diversity preservation mechanism. Standard GA (select, crossover, and mutation) is combined with non-dominated sorting in NSGA-II. Additionally, a fitness parameter called \"Crowding Distance\" is included to account for the density of solutions surrounding a given solution. The algorithm in [4] was used to enhance existing equations. In particular, the NSGAII method was used to optimize two different objective functions, i.e., maximizing the coefficient of determination and minimizing the average absolute relative deviation. After the optimization phase, ten selection approaches drawn from the literature were compared to select the best solution on the Pareto frontier.