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Intelligence has long been an interesting and important topic in psychology and cognitive science. IQ is considered a basic measure of a person's cognitive abilities, which includes various aspects of reasoning, problem solving, memory, and overall intellectual ability. Considering the importance of IQ in cognitive and psychological evaluations, the main goal of this article was to provide a new and effective approach to improve the accuracy of estimating this measure through complex brain data processing. In this paper, we have analyzed and developed a hybrid model of GWO algorithm and CNN (GCNN) in order to estimate IQ using brain MRI images. The results of the experiments showed that the accuracy of the proposed model was significantly better than the traditional techniques, and this indicates the high capabilities of the model in interpreting complex medical data. By examining the results, we find that the accuracy of the proposed model with an estimation rate of 93.10% is better than other competing methods.

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In this paper, a modified hybrid three-term conjugate gradient (CG) method is proposed for solving unconstrained optimization problems. The search direction is a three-term hybrid form of the Hestenes-Stiefel (HS) and Liu–Storey (LS) CG parameters. It is established that the method ensures the sufficient descent property independent of line search techniques. The convergence analysis of the proposed method is carried out under standard assumptions for general functions. Numerical experiments on CUTEr problems and image denoising tasks demonstrate that our method outperforms existing approaches in terms of efficiency, accuracy, and robustness, particularly under high levels of salt-and-pepper noise.

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Prostate cancer is the most common cancer in men and the second leading cause of cancer-related death worldwide. Over the years, researchers from various fields, beyond medicine, have sought to expand their understanding of the disease to develop more effective treatments. Treatment planning for high-dose-rate (HDR) brachytherapy involves designing the trajectory of the radiation source to deliver sufficient doses to the target area while minimizing exposure to surrounding organs at risk (OAR) within clinically safe limits. Since the exact tumor volume is not known, the model uses gray numbers instead of tumor volume, which provides more accurate results.
In this study, four powerful multi-objective evolutionary algorithms (MOEAs) NSGA¹-II, PESA²-II, SPEA³-II, and MOPSO⁴ are employed. Instead of yielding a single best solution, these algorithms produce a set of Pareto-optimal solutions, each representing a trade-off where no one solution is definitively better than the rest. However, they demonstrate improved performance compared to other optimization methods. The results show that the MOPSO algorithm performs better than the other three powerful algorithms in terms of solution quality and maintaining diversity among solutions.
 

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The explosive growth of global e-commerce and the increasing complexity of last-mile logistics have made the strategic placement of smart lockers a critical concern in modern urban logistics systems. Conventional methods, which rely solely on Multi-Criteria Decision Making (MCDM) methods for obtaining solutions, suffer from several limitations when implemented in uncertain, significant, and multi-objective scenarios. This paper proposes a stochastic multi-objective optimisation model for the BWM, prioritising decision criteria, which is solved by combining a hybrid metaheuristic solution methodology. The proposed model optimizes both total cost and sustainability performance from economic, environmental, and social perspectives, as well as robustness to demand uncertainty. An empirical study using Babol City, Iran, is presented to test and demonstrate the proposed framework. Candidate locker location and demand areas were examined based on expert-elicited criteria weights, with the preparation of a multi-objective mixed-integer programming model. In order to alleviate the computation burden, a combined structure of NSGA-II and LNS (referred to as NSGA-II+LNS) was proposed, which outperforms classical evolutionary algorithms in terms of convergence into the Pareto frontier. Factual results indicate that factoring in economic affordability, accessibility, and environmental impact is key to optimal locker capacity design. Robust solutions under demand fluctuation can save up to 18% more on service reliability, providing strong deterministic answers. This article makes the following theoretical and practical contributions: (i) a novel sustainable-oriented, deterministic model for smart locker location is proposed; (ii) advanced metaheuristics are integrated with MCDM in urban logistics, whereas fewer studies have focused on integrating them; and (iii) policy implications are suggested not only to policymakers but also to logistics operators who want robust last-mile delivery strategies..