Particle Swarm Optimization - Science topic

For the past two decades, heart disease has been classified as one of the main causes of mortality globally. Fortunately, most researchers focused on data mining techniques, which play an important role in accurately predicting heart disease to develop their models. In this paper, by combining particle swarm optimization (PSO) and modified random f...

Ensuring the freshness and quality of cauliflower during storage and transportation is essential due to its high perishability. This study harnesses the power of machine learning to predict the quality and shelf-life of cauliflower under cost-effective vacuum and modified atmosphere packaging (MAP) techniques. By investigating key parameters such a...

Next Generation communications aim to improve Quality of Service (QoS) via ultra-reliable low latency communication (URLLC), enhanced Mobile Broadband (eMBB), and massive machine type communication (mMTC). However, interference poses challenges in wireless domains. Interference mitigation is a fundamental goal, enabling centralized routing-based in...

Adding fast charging stations (FCSs) and photovoltaic systems (PVs) to modern electrical distribution networks (EDNs) can cause problems like voltage fluctuations and more power loss. One way to solve these issues is to use a levy-flight-based improved Pufferfish optimization algorithm (IPOA), which can find better answers to the PVs and FCSs in ED...

Improving the level of human resource allocation in hospitals through algorithms is an effective way to deepen hospital reform. This paper elaborates on the importance of health human resource allocation and scheduling. Combined with the hospital’s human resource problem is modeled and its model is optimized. Based on particle swarm and 0-1 plannin...

Chinese-foreign cooperative education is an attempt to introduce internationalized education methods to improve the level of local students. Aiming at the problems of insufficient teachers and inadequate combination of Chinese and foreign courses faced by Guangxi universities in the process of cooperative education, this paper constructs a multi-ob...

Along with the deepening of the economic system reform, the reform in the field of education urgently needs to be deepened, and the value and potential of resource allocation need to be further explored. This paper trains the weights and thresholds of the network and optimizes the BP neural network by using the PSO algorithm instead of the gradient...

The development of microgrids has emerged as a strategic solution to address the challenges inherent in radial distribution networks (RDNs). By integrating a microgrid system on the low‐voltage side of the distribution feeders, it enhances the flexibility, reliability, and efficiency of power delivery to customers. Radial power distribution systems...

This research presents an innovative computational approach that merges artificial intelligence with multi-objective optimization techniques to enhance cement slag concrete design. The proposed framework integrates deep neural networks (DNN), gradient boosting machines (GBM), and extreme learning machines (ELM) with particle swarm optimization (PSO...

Introduction: The global phase 3 POETYK PSO-1 (NCT03624127) and POETYK PSO-2 (NCT03611751) trials of deucravacitinib, an oral, selective, allosteric tyrosine kinase 2 inhibitor, included psoriasis patients with a history of malignancy. This descriptive analysis evaluated malignancy events in deucravacitinib-treated patients with malignancy prior to...

Alumina has found wide application in technological and engineering fields. However, due to the environmental effects of the traditional Bayer process of production, there is a need for a more eco-friendly and cost-effective procedure. The optimization of alumina extraction from microcline in nitric acid (HNO3) solution is considered in this work....

The rapid advancement of low-carbon technologies, such as wind and nuclear power, introduces critical ethical challenges, including conflicts between environmental protection, land use, and community rights. This study presents a comprehensive framework to address these conflicts through data-driven optimization and ethical analysis. First, a robus...

Lithium dendrite growth and the resulting safety concerns hinder the application of lithium metal. Compared with single metal or medium entropy alloys, high‐entropy alloys (HEAs) are a promising solution to solve the challenges of lithium metal anodes due to their unique properties. However, designing HEA layer with appropriate elements and proport...

Cooperative communication system (CCS) involves collaboration among sensor nodes to transmit data more effectively, especially in scenarios with limited resources or challenging environmental conditions. Optimizing the total error rate (TER) for cooperative communication in wireless sensor networks (WSN) is a critical task to enhance the reliabilit...

Advancing smart grids by using clean energy resources increases the integrity of distributed generators (DGs) containing power distribution networks. DGs often lead to increased voltages at their common coupling point. Typical voltage regulators, such as the on-load tap changers (OLTCs), cannot address these issues without coordinating with DGs. Th...

Gray Wolf Optimization (GWO), inspired by the social hierarchy and cooperative hunting behavior of gray wolves, is a widely used metaheuristic algorithm for solving complex optimization problems in various domains, including engineering design, image processing, and machine learning. However, standard GWO can suffer from premature convergence and s...

Background Information: The improved security framework for cloud healthcare systems presented in this research combines Zero-Knowledge Proofs (ZKP), Weight-Improved Particle Swarm Optimization (WIPSO), and fine-grained access control. By improving privacy and data security, the framework guarantees safe access to private medical data. Objectives:...

Influence maximization (IM) is a pivotal challenge in social network analysis, which aims to identify a subset of key nodes that can maximize the information spread across networks. Traditional methods often sacrifice solution accuracy for spreading efficiency, while meta-heuristic approaches face limitations in escaping local optima and balancing...

The Internet of Things (IoT) and Wireless Sensor Networks (WSNs) heavily rely on the lifetime of sensor nodes, which is inversely proportional to transmission power. Nodes with greater separation demand higher transmission power, while those closer together require less power. In practice, node placement varies significantly due to diverse terrain...

The growing global demand for clean, sustainable energy has driven extensive research into renewable energy technologies, with solar energy emerging as a highly promising solution. Solar photovoltaic (PV) systems are increasingly adopted for their ability to convert sunlight into electricity, providing an environmentally friendly alternative to fos...

Full waveform inversion (FWI) has become the standard for high resolution subsurface imaging, in both academia and the industry. FWI is formulated as a data fitting procedure, where the fit between the observed and the synthetic seismograms is improved iteratively. The synthetic seismograms are computed through the numerical solution of a wave equa...

This work reflects our commitment to advancing innovation in EEG-based emotion recognition using cutting-edge machine learning techniques like Particle Swarm Optimization (PSO) and Convolutional Neural Networks (CNN). A big thank you to my co-authors Raji V., Maheswari S., Kanmani P., K.S. Kavin, and Benisha Janice J. for their collaboration and su...

This paper considers two dynamic load models that are widely used in industry to account for induction motor behavior: CMLD and CLOD. These models must be parametrized for the specific utility system in a general way so that they can be used in planning studies and provide a conservative but realistic representation of load behavior. This study con...

Aero-engine turbine components have complex structures, harsh service load environments, and strong nonlinear structural responses, so it is difficult to establish the explicit limit state function, which is a typical nonlinear implicit function reliability analysis problem. To address this challenge, based on Particle Swarm Optimization and a Back...

Urban drone applications require efficient path planning to ensure safe and optimal navigation through complex environments. Drawing inspiration from the collective intelligence of animal groups and electoral processes in human societies, this study integrates hierarchical structures and group interaction behaviors into the standard Particle Swarm...

To address the electric vehicle (EV) charging scheduling problem in rural distribution networks, this study proposes a novel two-phase optimization strategy that combines particle swarm optimization (PSO) and Q-learning for global optimization and real-time adaptation. In the first stage, PSO is used to generate an initial charging plan that minimi...

The growing global demand for clean, sustainable energy has driven extensive research into renewable energy technologies, with solar energy emerging as a highly promising solution. Solar photovoltaic (PV) systems are increasingly adopted for their ability to convert sunlight into electricity, providing an environmentally friendly alternative to fos...

Hyperuricemia has seen a continuous increase in incidence and a trend towards younger patients in recent years, posing a serious threat to human health and highlighting the urgency of using technological means for disease risk prediction. Existing risk prediction models for hyperuricemia typically include two major categories of indicators: routine...

The increasing integration of electric vehicles (EVs) into the power grid presents a promising opportunity to enhance grid stability. This study introduces an optimization-driven EV Aggregator as a Voltage Control Ancillary Service (EVA-VCAS) system, which leverages Vehicle-to-Grid (V2G) technology to mitigate voltage sags. By employing advanced op...

Urban infrastructure systems, such as water supply and transportation networks, are highly interdependent, making them susceptible to cascading disruptions. This paper introduces a bi-level optimization framework designed to coordinate water supply network repairs while minimizing traffic impacts. The framework integrates a dynamic traffic assignme...

With the continuous expansion of the power system scale and the increasing growth of electricity demand, accurate load forecasting is of great significance for the safe, stable operation and economic dispatch of the power system. Traditional load forecasting methods have certain limitations in handling complex load data and are difficult to meet ac...

The aim of this study is to examine circular tunnel stability by investigating the influence of the spatial variability of rock masses by using the Hoek–Brown model, random field theory, and random adaptive finite element limit analysis (RAFELA). The analysis investigates the influences of six input parameters, including the cover depth ratio (C/D)...

Polarized imaging is capable of displaying the morphological and physicochemical properties of a target and detecting invisible targets, while a large field-of-view (FOV) is of great benefit in obtaining more information about the scene in one imaging session. In this study, we propose a wide-angle orthogonal polarized metalens (WOPM) that works in...

The growing global demand for clean, sustainable energy has driven extensive research into renewable energy technologies, with solar energy emerging as a highly promising solution. Solar photovoltaic (PV) systems are increasingly adopted for their ability to convert sunlight into electricity, providing an environmentally friendly alternative to fos...

p class="ICST-abstracttext"> Accurate prediction of distributed photovoltaic (DPV) power generation is crucial for stable grid operation, yet existing methods struggle with the non-linear, intermittent nature of solar power, and traditional machine learning models face hyperparameter selection and overfitting challenges. This study developed a high...

The ultra-wideband (UWB) base station (BS) deployment pattern seriously affects mobile tag positioning accuracy, but the traditional classical deployment methods, such as rectangular and diamond deployment, cannot take into account the influence of non-line-of-sight (NLOS) occlusion, which leads to a blind area in positioning. In this paper, we pro...

With the widespread application of multi-objective optimization problems (MOPs) in fields such as engineering design, decision analysis, and resource management, traditional multi-objective optimization algorithms face challenges such as a single learning pattern and premature convergence when solving complex problems. To address these issues, this...

To achieve integrated and intelligent product manufacturing, it is essential to consider the impact of manufacturing on design, and build simulation modeling to evaluate performance accordingly. The digital twin maps the attributes, structure, and performance of the product in the above stages into the virtual world and builds a high-fidelity model...

In distribution grids, excessive energy losses not only increase operational costs but also contribute to a larger environmental footprint due to inefficient resource utilization. Ensuring optimal placement of photovoltaic (PV) energy systems is crucial for achieving maximum efficiency and reliability in power distribution networks. This research i...

Web page recommendation system has been emerging as the most important area in Service computing. Web pages are analyzed and selected for recommendation in order to favor end users while searching for information. Collaborative filtering and content based approaches are two predominant techniques for recommending web pages. Traditional Naive Bayes...

This study introduces the application of Stacking Ensemble Learning in petroleum engineering, marking a significant advancement in oil production rate forecasting. Unlike traditional forecasting methods, which often rely on single-model approaches with limited adaptability to complex, the methodology integrates multiple machine learning algorithms...

Coordinating various controllable distributed resources to reduce network losses is crucial to the secure and economical operation of modern power systems. This paper proposes a bi-level optimization model for power system loss reduction based on “source-grid-load-storage” coordinated optimization. The upper level aims to minimize the total annual...

This paper presents an efficient approach for developing miniature sub-6 GHz 5G MIMO antennas with enhanced bandwidth and high isolation. Utilizing the capabilities of particle swarm optimization (PSO) on a pixelated surface, an objective function is formulated to minimize the reciprocal of the bandwidth while ensuring that the reflection coefficie...

With the rapid advancement of digital technologies, personalized learning platforms have become increasingly important in vocational education. This study, based on the digital platform of Xiamen Nanyang Vocational College, leverages dynamic knowledge graphs, multi-objective optimization, and adaptive algorithms to provide students with personalize...

In this study, first-principles calculations in conjunction with the particle swarm optimization (PSO) algorithm structure search method were employed to investigate the stable phases of Ca-Pt intermetallic compounds under various pressure conditions. The previously reported CaPt5 phase and the hitherto unreported phases Ca3Pt and Ca2Pt were succes...

Image classification is essential in artificial intelligence, with applications in medical diagnostics, autonomous navigation, and industrial automation. Traditional training methods like stochastic gradient descent (SGD) often suffer from slow convergence and local minima. This research presents a hybrid Particle Swarm Optimization (PSO)-Genetic A...

The drilling process can result in irregular measurements due to unconsolidated geological formations, affecting the accuracy of wireline logging devices. This impacts the precision of elastic log measurements, such as velocity and density profiles, which are essential for reservoir characterization. The reliability of the wireline-logging tool is...

With the rapid development of the cold chain logistics industry, its high energy consumption and low operational efficiency have become increasingly prominent, seriously restricting the sustainable development of the industry. This study focuses on this and proposes a real-time monitoring system for cold chain logistics based on the Internet of Thi...

Skin cancer is a major global health concern and one of the deadliest forms of cancer. Early and accurate detection significantly increases the chances of survival. However, traditional visual inspection methods are time-consuming and prone to errors due to artifacts and noise in dermoscopic images. To address these challenges, this paper proposes...

Offshore wind turbines have garnered significant attention recently due to their substantial wind energy harvesting capabilities. Pitch control plays a crucial role in maintaining the rated generator speed, particularly in offshore environments characterized by highly turbulent winds, which pose a huge challenge. Moreover, hydraulic pitch systems a...

How do public sector organizations (PSO) adapt established dynamic capabilities (DC)? Our longitudinal study of two Norwegian municipalities reveals that public middle managers leverage their distinct dynamic managerial capabilities (DMC) to deploy and adapt organization-level DC through specific meso-level mechanisms. Our study makes two significa...

Maritime transportation significantly contributes to air pollution, especially in coastal cities. Air pollution represents the greatest health risk related to the environment in the European Union. Therefore, the European Commission published the European Green Deal, which introduces the rule of zero-emission requirements for ships at berths with t...

Due to that the complex mechanical faults of high‐voltage circuit breakers and the difficulty in extracting fault features, a fault diagnosis method combining Improved Particle Swarm Optimization enhanced Variational Mode Decomposition (IPSO‐VMD) with Kernel Fuzzy C‐Means and Support Vector Machine (KFCM‐SVM) is proposed. Initially, the vibration s...

Electric vehicle technologies present promising solutions for achieving energy conservation and emission reduction goals. However, efficiently distributing power across hybrid energy storage systems (HESSs) remains a major challenge in enhancing overall system performance. To address this, this paper proposes an energy management strategy (EMS) bas...

The university course scheduling problem (UCSP) is a challenging combinatorial optimization problem that requires optimization of the quality of the schedule and resource utilization while meeting multiple constraints involving courses, teachers, students, and classrooms. Although various algorithms have been applied to solve the UCSP, most of the...

This study introduced a novel type of biochar–titanate nanosheet (BC@TNS) composite for the selective adsorption of Pb(II) from wastewater containing various heavy metal ions. The biochar derived from lignin–carbon pyrolysis forms the scaffold, while titanate nanosheets coat it via an alkaline hydrothermal reaction. The synthesis was confirmed thro...

This paper proposes an improved Jellyfish Search algorithm, namely TLDW-JS, for solving the problem of optimal path planning of multi-robot collaboration in the multi-tasking of complex vertical farming environments. Vertical farming is an efficient way to solve the global food problem, but how to deploy agricultural robots in the environment const...

In this case study, a sigmoid function and particle swarm optimization (PSO) are proposed to design non-linear PI controller to control non-linear dynamic systems, which can decrease the effect of the external disturbances, non linearity's and uncertainty. Modern power systems are non-linear and complex and their operating conditions vary over a wi...

Designing control systems for islanded microgrids poses significant challenges due to the absence of inertia and parameter uncertainties. These factors increase the complexity of traditional methods when applied to highly nonlinear and interdependent systems. To address this issue, a novel Electric Eel Foraging Optimization (EEFO) technique is prop...

High efficiency and eco friendliness, proton exchange membrane fuel cells (PEMFCs) have become a good solution to cleaner energy solutions. However, due to the electrochemical complexity of PEMFCs and the limitations of existing optimization methods, accurately estimating PEMFC parameters to achieve optimal performance is still challenging. In this...

Power system stability is managed through various control loops, including the Automatic Voltage Regulator (AVR), which regulates the terminal voltage of synchronous generators. This study integrated Fuzzy Logic Control (FLC) and a Proportional–Integral–Derivative controller with Filtered derivative action (PIDF) to propose a hybrid Fuzzy PIDF cont...

The increasing energy demand and initiatives to lower carbon emissions have elevated the significance of renewable energy sources. Photovoltaic (PV) systems are pivotal in converting solar energy into electricity and have a significant role in sustainable energy production. Therefore, it is critical to implement maximum power point tracking (MPPT)...

Social media has attracted society for decades due to its reciprocal and real-life nature. It influenced almost all societal entities, including governments, academics, industries, health, and finance. The Social Network generates unstructured information about brands, political issues, cryptocurrencies, and global pandemics. The major challenge is...

This study introduces an Enhanced Local Search (ELS) technique integrated into the Bee Colony Optimization (BCO) algorithm to address the Economic Dispatch (ED) problem characterized by a continuous cost function. This paper combines Lambda Iteration and Golden Section Search with Bee Colony Optimization (BCO) into a more efficient method called En...

With a focus on reducing building energy consumption, approaches that simultaneously optimize multiple passive design parameters in industrial buildings have received limited attention. Most existing studies tend to examine building geometry or individual design parameters under limited scenarios, underscoring the potential benefits of adopting a c...

structural demands of modern construction is becoming urgently critical. The proposed model will handle the lack of sustainability and mechanical performance of the existing approaches. Specifically, they are not capable of dynamically adapting up to the changing environmental conditions and the intrinsic complexity of optimizing the material prope...

The study presents a new optimization method that merges Backpropagation Neural Networks (BPNN) with an enhanced Energy Valley Optimizer (EVO) algorithm to elevate the performance of multi-input nonlinear systems. Traditional optimization methods like Gradient Descent and Particle Swarm Optimization (PSO) face issues with slow convergence and local...

The objective of this study is to produce porous, sustainable ceramic adsorptive aggregates for the improvement of water quality and water treatment applications. Three types of clay, Natural Zeolite (NZ), and Spent Coffee Grounds (SCG) are used in the synthesis of aggregates with high adsorptive capacities. Various characterizations are conducted,...

Backbreak is an undesirable outcome in blasting operations caused by factors such as equipment failure, improper fragmentation, unstable mine walls, reduced drilling efficiency, and other issues that contribute to increased mining operation costs. To overcome these problems effectively, this study developed a least square support vector machine (LS...

With the continuous development of industrial automation, diesel engines play an increasingly important role in various types of construction machinery and power generation equipment. Improving the dynamic and static performance of the speed control system of single-cylinder diesel engines can not only significantly improve the efficiency of the eq...

The major challenges in designing a Hybrid Renewable Energy System (HRES) include selecting appropriate renewable energy sources and storage systems, accurately sizing each component, and defining suitable optimization criteria. This study addresses these challenges by employing Particle Swarm Optimization (PSO) within a multi-criteria optimization...

This paper investigates the application of Neural Network Backstepping Control (NN-BSC) for enhancing the rotational speed control of Oscillating Water Column (OWC) wave energy systems. Traditional control methods face limitations when dealing with nonlinearities, irregular wave conditions, and actuator disturbances. To address these challenges, th...

In an era of increasing sophistication and frequency of cyber threats, securing Internet of Things (IoT) networks has become a paramount concern. IoT networks, with their diverse and interconnected devices, face unique security challenges that traditional methods often fail to address effectively. To tackle these challenges, an Intrusion Detection...

The representation of optimization problems and algorithms in terms of numerical features is a well-established tool for comparing optimization problem instances, for analyzing the behavior of optimization algorithms, and the quality of existing problem benchmarks, as well as for automated per-instance algorithm selection and configuration approach...

Celem pracy jest przedstawienie problemu wariantowania przedsięwzięć informatycznych zagrożonych niepowodzeniem w aspekcie Problemu Spełnienia Ograniczeń (PSO). Deklaratywna struktura modelu wiąże obszary funkcjonalności przedsiębiorstwa oraz realizowanego w nim projektu. Wymienione obszary funkcjonalności modelowane są w postaci PSO, zawierają one...

This paper presents the simulation and controller optimization of a quadrotor Unmanned Aerial Vehicle (UAV) system. The quadrotor model is derived adopting the Newton-Euler approach, and is intended to be constituted by four three-phase Permanent Magnet Synchronous Motors (PMSM) controlled with a velocity control loop-based Field Oriented Control (...

This article proposes a displacement reducer based on distributed compliant mechanisms to improve the motion resolution of actuators commonly used in precision operation systems that require high-precision control and positioning, such as micro-grippers, biological manipulation, and micro-alignment mechanisms. Distributed compliance significantly d...

In modern power systems, fluctuations in load present ongoing challenges, making Load frequency control (LFC) an essential part of maintaining system stability and efficiency. This paper explores a method that combines traditional PID control with the Particle Swarm Optimization (PSO) algorithm to improve frequency regulation in interconnected hydr...

Tracking the peak power output of solar photovoltaic modules poses a significant challenge in contemporary times, that too under variable climatic conditions. Despite the availability of various Maximum Power Point Tracking (MPPT) methods, each method carries its own set of limitations. Many of these constraints can be effectively addressed by leve...

One of the major issues for highly crowded metropolitan cities is road traffic congestion. Road traffic congestion causes traffic jams, which affects major services like ambulance service. As a solution, this paper has come up with "Intelligent automatic traffic signal control for ambulance". To implement the intelligent traffic signal control syst...

The integration of distributed generation (DG) and high-voltage direct current (HVDC) facilities into a power system results in altered transient responses compared to traditional AC-based power systems. This study investigates the transient stability of AC-DC hybrid power systems incorporating DG and HVDC facilities. The objective of this study is...

We investigate portfolio optimization in financial markets from a trading and risk management perspective. We term this task Risk-Aware Trading Portfolio Optimization (RATPO), formulate the corresponding optimization problem, and propose an efficient Risk-Aware Trading Swarm (RATS) algorithm to solve it. The key elements of RATPO are a generic init...

The agricultural industry significantly relies on autonomous systems for detecting and analyzing rice diseases to minimize financial and resource losses, reduce yield reductions, improve processing efficiency, and ensure healthy crop production. Advances in deep learning have greatly enhanced disease diagnostic techniques in agriculture. Accurate i...

For optimization algorithms, the most important consideration is their global optimization performance. Our research is conducted with the hope that the algorithm can robustly find the optimal solution to the target problem at a lower computational cost or faster speed. For stochastic optimization algorithms based on population search methods, the...

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...

With the rapid global proliferation of electric vehicles (EVs), their integration as a significant load component within power systems increasingly influences the stable operation and planning of electrical grids. However, the high uncertainty and randomness inherent in EV users’ charging behaviors render accurate load forecasting a challenging tas...