In the last decades, the field of global optimization has experienced significant growth, leading to the development of various deterministic and stochastic algorithms designed to tackle a wide range of optimization p...
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In recent years, quadrotors have emerged as essential roles in robotics, and their diversity and usefulness emphasize their importance. This research work presents an in-depth analysis of a quadcopter in terms of mode...
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In recent years, quadrotors have emerged as essential roles in robotics, and their diversity and usefulness emphasize their importance. This research work presents an in-depth analysis of a quadcopter in terms of modeling, control, and optimization, where central to the operation of quadcopters and all robotics systems is the idea of stability response. This paper discusses the possibility of providing quadcopter stability by demonstrating the impact of the fractional controller in sensitive systems. The five fractional parameters for each engine are also improved using the Bonobo optimization (BO) algorithm. The optimized results in this paper are compared with the algorithms used, such as Genetic Algorithm (GA), Particle Swarm optimization (PSO), and Grey Wolf optimization (GWO). The fractional-order proportional integral derivative (FOPID) controller has greater control power compared to its classic counterpart, PID control, as it provided improvement in minimizing overshoot by 90%, and it showed great improvement in settling and rising times using GWO 25% and BO 50% with some superiority of BO. By examining both the advantages and constraints inherent in these methodologies, we seek to advance the field forward, promoting more breakthroughs in this crucial area.
Fuzzy mathematical theory is widely used, fuzzy optimization is a branch of fuzzy mathematical theory, the significant application area is artificial intelligence in computer science, especially machine learning (deep...
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In this paper, we presented a full-Newton short-step interior-point algorithm, which is based on a new algebraically equivalent transformation technique, for a linear optimization problem. This technique offers a new ...
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Nowadays, online optimization for power systems has gained increasing attention due to many time-varying scenarios in practical applications. This paper proposes a novel feedback-based online algorithm for power syste...
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In the field of bridge design, multi-objective optimization problems have attracted much attention due to their complexity and multiple solutions. The limitations of existing optimization algorithms in dealing with mu...
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In this paper, we consider nonconvex uncertain vector optimization problems, and discuss the properties of their robust efficient solution sets. First, existence conditions of the robust efficient solutions under cons...
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Constrained multi-objective optimization problems are ubiquitous in real life. However, the presence of constraints makes the feasible domain complex, discontinuous and narrow. Consequently, solving multi-objective op...
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In our data-driven world, the healthcare sector faces significant challenges in the early detection and management of Non-Communicable Diseases (NCDs). The COVID-19 pandemic has further emphasized the need for effecti...
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In our data-driven world, the healthcare sector faces significant challenges in the early detection and management of Non-Communicable Diseases (NCDs). The COVID-19 pandemic has further emphasized the need for effective tools to predict and treat NCDs, especially in individuals at risk. This research addresses these pressing concerns by proposing a comprehensive framework that combines advanced data mining techniques, feature selection, and meta-heuristic optimization. The proposed framework introduces novel hybrid algorithms, including the Hierarchical Genetic Multiple Reduct Selection Algorithm (H-GMRA) and the Customized Function-based Particle Swarm optimization with Rough Set Theory for NCD Feature Selection (CPSO-RST-NFS). These algorithms aim to address the challenges of feature selection, computational complexity, and disease classification accuracy. H-GMRA outperforms traditional methods by identifying minimal feature sets with high dependency ratios. CPSO-RST-NFS combines meta-heuristic optimization with feature selection, resulting in improved efficiency and accuracy. Through extensive experimentation on diverse NCD datasets, this research demonstrates the framework's ability to select informative features, improve classification accuracy, and contribute to better patient outcomes. By bridging the gap between computational efficiency and disease classification accuracy, this work offers valuable insights for healthcare practitioners and data analysts, ultimately advancing the field of NCD research. The proposed framework presents a significant step towards enhancing the early detection and management of NCDs, offering hope for more precise clinical predictions and improved patient care.
Real-world network-optimization problems often involve uncertain parameters during the optimization phase. Stochastic optimization is a key approach introduced in the 1950s to address such uncertainty. This paper pres...
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