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Introduction. Metaheuristic algorithms are effective for solving complex power system optimization problems characterized by nonlinearity, multimodality, and high dimensionality. Nature-inspired strategies based on adapt...

Abstract In this paper, it is presented a general optimization methodology for improving empirical models predicting Okumura-Hata, Cost-231, ECC-33, and Egli using Genetic and Differential Evolution Algorithms. The metho...

Abstract This paper describes a method using Quantum Particle Swarm Optimization to obtain a broadband frequency invariant pattern for synthesis of nonuniformly spaced linear array of isotropic antennas. Two cases relate...

Abstract This paper describes a new design methodology for reconfigurable printed circuits with limited size, using an improved hybrid particle swarm optimization (HPSO) algorithm, reducing the search space by the defini...

Hypertension is a major chronic disease worldwide and in Iraq. Baghdad especially suffers from it due to rapid urbanization, dietary changes, and lifestyle shifts. We lack local data on hypertension factors in Baghdad co...

Devices based on two-dimensional photonic-crystal nanocavities, which are defined by their air hole patterns, usually require a high quality (Q) factor to achieve high performance. We demonstrate that hole patterns with ...

BackgroundLast-mile logistics accounts for approximately 28% of total logistics costs and is a major contributor to urban carbon emissions. The integration of drones with electric trucks offers a path toward more efficie...

Abstract The Purpose of this research is the development Direct Search Methods for minimizing the objective function; The Developed Methods reduces the approach time to the minimum through two techniques: 1) by reducing...

To address the issues of low solution accuracy, premature convergence, and susceptibility to local optima in the Parrot Optimization (PO) algorithm, a Reinforced Multi-strategy Parrot Optimization (RMPO) algorithm has be...

Background: Deep learning has transformed medical imaging by enabling earlier and more accurate disease diagnosis. Lesion and tumor segmentation, essential for analyzing and tracking morphological changes, is commonly do...

The genetic algorithm (GA) is a widely recognized optimization technique for addressing complex and NP-hard problems; however, its effectiveness is often hampered by issues such as premature convergence and population di...

There is an increasing need for the injection to the grid of renewable energy; therefore, to evaluate the optimal location of new renewable generation is an important task. The primary purpose of this work is to develop ...

Abstract Feature selection is a critical step in machine learning, as it helps minimize redundant features, thereby enhancing model performance and interpretability. In practice, partially labeled datasets are common. Wh...

Implicit learning is a fundamental cognitive process whose identification is critical for understanding human cognition and developing innovative training methodologies. We propose a generalizable feature selection and s...

Abstract Grid-connected renewable energy systems with high penetration rates exacerbate frequency fluctuations in microgrids, making traditional control methods ill-suited for random source-load variations. This paper pr...

This paper proposes an innovative automated multiphysics Microelectromechanical systems (MEMS) co-optimization platform integrating the embedded reduced-order modeling offered by Coventor MEMS+ with MATLAB’s numerical ...

Abstract With the outward expansion of university campuses toward suburban areas, the modern campus has evolved into an increasingly independent social space, providing an organizational setting in which users’ daily a...

Abstract The precise forecasting of the Remaining Useful Life (RUL, expressed in hours) of aircraft engine-like systems using a synthetic dataset designed to mimic real engine degradation patterns is a key factor in sett...

Abstract The problem of optimizing a certain systems as a function of time may be formulated in terms of maximizing or minimizing a non-linear cost function. It was found that a Dynamic Programming (DP) algorithm was par...

This study develops an innovative framework for the multi-objective optimization of multi-channel supply chains under uncertainty. The framework integrates the D-number model, an extension of Dempster–Shafer Theory (DS...

Abstract This paper presents the optimization performance of non-uniform linear antenna array with optimized inter-element spacing and excitation amplitude using Particle Swarm Optimization (PSO). The aim of the proposed...

Artificial intelligence (AI) is increasingly used to design and optimize small molecules. However, most current workflows remain centered on drug-candidate objectives, such as potency, ADMET, and drug-likeness, rather th...

Organizations increasingly face uncertainty in workforce planning, driven by shifting skill demands, rising turnover risks, and pressure to optimize labor investments. Traditional human capital decision models struggle t...

Abstract In the realm of engineering practice, various factors such as limited availability of measurement data and complex working conditions pose significant challenges to obtaining accurate load spectra. Thus, accurat...