Optimization Algorithm Research Articles

Improved photovoltaic energy production under partial shading using an innovative MPPT controller based on Flying Squirrel Search Optimization algorithm

This paper explores the issue of partial shading (PS) in photovoltaic systems and proposes a solution using the Flying Squirrel Search Optimization (FSSO) algorithm. PS results in power losses, and our study aims to enhance photovoltaic energy production by introducing a new Maximum Power Point Tracking (MPPT) controller based on the FSSO algorithm. This paper presents a controller designed to efficiently track the maximum power in the presence of partial shading. The controller uses the Modified ODD-EVEN (MOE) configuration, the Modified Symmetrical Array (MSA), and the Total-Cross-Tied (TCT). The performance of the FSSO algorithm was compared with that of GWO and PSO. A bidirectional DC-DC converter was integrated to connect the PV system to a battery. The proposed methods were also applied to an electric vehicle powered by a PV battery. The FSSO proposed in this study demonstrates an efficiency of over 99%, outperforming other methods in tracking the maximum power point and converging faster to the overall maximum power point. The results indicate that convergence time can be improved from 16% to over 60%. Furthermore, the proposed MPPT technique effectively minimizes over 80% of random oscillations. The proposed method reduces losses and maximizes fill factor compared to alternative algorithms, based on two observed shading cases. When connecting the system to a battery, the FSSO algorithm outperforms the PSO by more than 34.78% and the GWO by 49.02%. Although slightly inferior to the PSO, the FSSO still performs significantly in the context of an electric vehicle powered by a photovoltaic battery.

Identifying sources of bias when testing three available algorithms for quantifying white matter lesions: BIANCA, LPA and LGA.

Brain magnetic resonance imaging frequently reveals white matter lesions (WMLs) in older adults. They are often associated with cognitive impairment and risk of dementia. Given the continuous search for the optimal segmentation algorithm, we broke down this question by exploring whether the output of algorithms frequently used might be biased by the presence of different influencing factors. We studied the impact of age, sex, blood glucose levels, diabetes, systolic blood pressure and hypertension on automatic WML segmentation algorithms. We evaluated three widely used algorithms (BIANCA, LPA and LGA) using the population-based 1000BRAINS cohort (N = 1166, aged 18-87, 523 females, 643 males). We analysed two main aspects. Firstly, we examined whether training data (TD) characteristics influenced WML estimations, assessing the impact of relevant factors in the TD. Secondly, algorithm's output and performance within selected subgroups defined by these factors were assessed. Results revealed that BIANCA's WML estimations are influenced by the characteristics present in the TD. LPA and LGA consistently provided lower WML estimations compared to BIANCA's output when tested on participants under 67 years of age without risk cardiovascular factors. Notably, LPA and LGA showed reduced accuracy for these participants. However, LPA and LGA showed better performance for older participants presenting cardiovascular risk factors. Results suggest that incorporating comprehensive cohort factors like diverse age, sex and participants with and without hypertension in the TD could enhance WML-based analyses and mitigate potential sources of bias. LPA and LGA are a fast and valid option for older participants with cardiovascular risk factors.

Secure and Reliable Intrusion Detection Scheme for Software-Defined Networking Using LFTS-Rnn and PC-JTFOA

In recent times, Software Defined Networking (SDN) has gained immense popularity among communication networks due to its high network connectivity and security. But, owing to the lack of sufficient security schemes and dynamic access control, conventional techniques could be more efficient. Hence, this paper proposes a secure intrusion detection scheme for SDN utilizing LogishFTS-based Recurrent Neural Networks (LFTS-RNN) and Poisson Clumping Japanese Tree Frog Optimization Algorithm (PC-JTFOA). Initially, by using a username, password, and Signed Access Control (SAC), the registered users log in to the network. The Entropy Makwa-based Digital Signature Algorithm (EMDSA) is utilized to identify the attackers during the login process. Then, the Intrusion Detection System (IDS) is trained using the historical dataset. The critical features are extracted in IDS; subsequently, by using the PC-JTFOA, the optimal features are selected. Likewise, the selected features are inputted into the LFTS-RNN to recognize the intruders in the application plane. Moreover, the process of data security and load balancing is undergone by the data. Afterward, the balanced data is subjected to IDS to detect the attackers in the control plane. According to experimental outcomes, the proposed technique obtained a higher prominence with an accuracy of 98.35%.

Enhancing Asian Indigenous Language Processing through Deep Learning-based Handwriting Recognition and Optimization Techniques

Asian indigenous language or autochthonous language is a language which is native to a region and spoken by indigenous people in Asia. This language is a linguistically different community created in the region. Recently, researchers in handwriting detection studies comparing with indigenous languages have attained important internet amongst the research community. A new development of artificial intelligence (AI), natural language processing (NLP), cognitive analytics, and computational linguistics (CL) find it helpful in the analysis of regional low-resource languages. It can be obvious in the obtainability of effectual machine detection methods and open access handwritten databases. Tamil is the most ancient Indian language that is mostly exploited in the Southern part of India, Sri Lanka, and Malaysia. Tamil handwritten Character Recognition (HCR) is a critical procedure in optical character detection. Therefore, this study designs a Henry Gas Solubility Optimization with Deep Learning-based Handwriting Recognition Model (HGSODL-HRM) for Asian Indigenous Language Processing. The proposed HGSODL-HRM technique relies on computer vision and DL concepts for automated handwriting recognition in the Tamil language, which is one of the popular indigenous languages in Asia. To accomplish this, the HGSODL-HRM technique employs a capsule network (CapsNet) model for feature vector generation with the HGSO algorithm as a hyperparameter optimizer. For the recognition of handwritten characters, wavelet neural network (WNN) model is exploited. Finally, the WNN parameters can be optimally chosen by sail fish optimizer (SFO) algorithm. To demonstrate the promising results of the HGSODL-HRM system, an extensive range of simulations can be implemented. The simulation outcomes stated the betterment of the HGSODL-HRM system compared to recent DL models.

Multi-objective parameter matching of elastic ring for rotor system by using particle swarm optimization method

PurposeElastic rings served as the elastic supporting elements which have been extensively used in the aeroengines for maneuverable planes with high overloading. However, under extreme conditions, the elastic ring contacts the bearing seat, causing elastic ring failure. Therefore, it is necessary to optimize the matching parameters of the elastic ring in order to suppress the occurrence of elastic ring failure under harsh working conditions.Design/methodology/approachIn this paper, a rotor system supported by elastic rings is researched and a multi-objective parameter matching method of elastic ring is proposed, considering the elastic ring failure, rotor system’s frequency forbidden zone and rotor system’s dynamic response. Then, the particle swarm optimization algorithm is used to dynamically constrain the parameter matching space and obtain the ideal solution for the elastic ring parameter matching.FindingsBy analyzing the elastic ring’s matching results (different unbalanced forces and disk masses), the relationship between the trend of Pareto front changes and rotor system parameters is studied. In addition, the rotor system’s dynamic characteristics before and after parameter matching are analyzed.Originality/valueThis article provides guidance for the design of elastic rings by matching the parameters of elastic rings.

The hybrid approach of genetic algorithm and particle swarm optimization on reduced weld line defect in plastic injection molding

Weld lines are a serious defect observed in plastic injection molded parts, impacting both their cosmetic appearance and mechanical properties. Controlling the conditions of plastic injection is crucial to mitigate these weld lines. This study introduces a novel approach to identify polypropylene injection molding (PIM) conditions aimed at reducing weld lines in polypropylene parts. The PIM conditions considered in this study include melt temperature, injection pressure, packing pressure, packing time, and cooling time. An orthogonal array Taguchi L27 design was employed for the experimental setup, producing 27 polypropylene parts with varying combinations of process conditions. The width of weld lines generated on the parts’ surfaces was measured using an optimum microscope for all trials. Parametric analysis was conducted using response surface plots and contour plots to estimate the process conditions yielding minimum weld lines. Analysis of variance and regression analysis were employed to interpret the experimental data, with the resulting regression equation used to predict weld lines for a set of PIM process conditions. Finally, two efficient optimization algorithms, genetic algorithm (GA), and particle swarm optimization (PSO), were implemented using MATLAB programming to estimate the optimum process conditions for minimizing weld lines. The GA and PSO predicted weld line widths of 6.12302 μm and 6.123 μm, respectively, representing an 18.51% improvement in results. These findings demonstrate that the novel approach presented in this study can be effectively and reliably applied to address plastic product defects in the industry.

Thermodynamic modeling and analysis of a Carnot battery system integrating calcium looping thermochemical energy storage with coal-fired power plant

Long-term energy storage and carbon capture technologies are pivotal in managing renewable energy surpluses and achieving carbon neutrality. This paper proposes a Carnot battery system integrating calcium-looping thermochemical energy storage with a coal-fired power plant. The system utilizes excess electricity from the grid for energy input, facilitating long-term energy storage, achieving carbon capture, and reducing coal consumption in the power plant. An optimization framework is developed, incorporating component thermodynamic models and optimization algorithms, to maximize the coal savings in plants and energy efficiencies of the Carnot battery system. During the energy release process, the carbonator partially replaces the reheat load of the boiler, necessitating retrofitting of the boiler’s heating surface to ensure normal operation. The base system demonstrates a CO2 capture capacity of 3.23 MJ/kg and a reduction in coal consumption by 7.07 %. The round-trip efficiency and comprehensive efficiency of the base system are 36.93 % and 37.91 %, respectively. The relatively low energy efficiency is primarily due to the deactivation of circulating adsorbents and the limited efficiency of the subcritical coal-fired power plant. By incorporating an additional recarbonation step to mitigate adsorbent deactivation, the system’s comprehensive efficiency is improved to 42.20 %. Further improvements in system efficiency can be achieved by using modified adsorbents with high cycling stability instead of natural limestone and by coupling the system with more efficient ultra-supercritical units instead of the investigated subcritical units. This study offers valuable insights into the development of long-term energy storage solutions and multifunctional Carnot battery technology.

Research on Agricultural Product Price Prediction Based on Improved PSO-GA

The accurate prediction of scallion prices can not only optimize supply chain management and help related practitioners and consumers to make more reasonable purchasing decisions, but also provide guidance for farmers’ planting choices, thus enhancing market efficiency and promoting the sustainable development of the whole industry. This study adopts the idea of decomposition–denoising–aggregation, using three decomposition and denoising techniques combined with three single prediction models to form a base model. Various base models are divided into different combinations based on whether the computational structure is the same or not, and the optimal weights of the combinations are determined by using the improved particle swarm optimization–genetic algorithm (PSO-GA) optimization algorithm in different combinations. The experimental results show that the scallion price in Shandong Province from 2014 to 2023 shows an overall upward trend, and there is a cyclical and seasonal fluctuation pattern of “high in winter and low in summer”; the semi-heterogeneous-PSO-GA model reduces the MAPE by 49.03% and improves the directional accuracy by 41.52%, compared to the optimal single prediction model, ARIMA. In summary, the combined model has the most accurate prediction and strong robustness, which can provide ideas and references for the difficult problem of determining the optimal weights of the combined model in the field of predicting the prices of agricultural products.

Grasshopper platform-assisted design optimization of fujian rural earthen buildings considering low-carbon emissions reduction

This work aims to explore optimization methods for the design of earthen buildings in rural Fujian to achieve low-carbon emissions and improve the structural stability of earthen buildings. First, parametric modeling and optimization algorithms are employed through the Grasshopper platform. An intelligent earthen building design is created by combining the optimization of factors such as the structure of earthen buildings, building materials, and orientation. Then, a comparison is made with the unoptimized, energy-efficient, and carbon emission reduction designs. Finally, the work concludes that the proposed design significantly optimizes the total carbon emissions, energy consumption, structural stability, and economic aspects. The proposed design scheme achieves the highest carbon emission reduction effect, with a reduction rate of 34.64%. The proposed design exhibits lower maximum stress and higher minimum safety factor in terms of structural stability compared to other scenarios, along with smaller structural displacement. It also performs well in terms of initial investment, annual operating costs, and construction period. The significance of this work lies in providing scientific guidance for the design and construction of rural earthen buildings, promoting the organic integration of rural development with low-carbon initiatives. This indicates that the use of intelligent optimization methods for earthen building design is feasible and can yield positive results in practice.

Bayesian optimization algorithms for accelerator physics

Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques toward solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design. Published by the American Physical Society 2024

Design and optimization strategy of electricity marketing information system supported by cloud computing platform

This paper provides an in-depth discussion on the comprehensive requirements analysis, design implementation, algorithm optimization, and experimental evaluation of an electric power marketing information system, aiming to build a modern information system that is efficient, secure, and user-friendly. In the requirements analysis phase, the importance of business process optimization, data management analysis, security compliance, system integration and scalability is emphasized, while the diversified needs of end customers are considered. For the design and implementation part, the system architecture is based on microservices and cloud-native technologies to ensure high performance and security; and modularized development is achieved through Spring Boot, Vue.js and other technology stacks. For algorithm optimization, LSTM is used for power demand prediction and anomaly detection by combining integrated learning and self-encoder, which improves the prediction accuracy and anomaly identification capability. Experimental evaluation shows that the system demonstrates good performance, security and scalability in cloud computing environment, and the cost-effectiveness is significantly better than traditional deployment.

Graphic Characterization of Terrain Using Information Computational Methods

As is well known, automated pattern recognition and satellite data analysis include a broad spectrum of pattern recognition. Image classification is a crucial remote sensing technology that facilitates the automatic understanding of massive amounts of data. Numerous researchers have put a great deal of effort into this field,and their results are impressive. In order to distinguish between diverse terrain classes, such as water, urban,rocky, vegetation, and barren, we are examining a variety of nature-inspired strategies in this article. the many methods we are using to do this, including the hybrid algorithms of Particle Swarm Optimization (PSO),Artificial Bee Colony (ABC) and Biogeography Based Optimization (BBO), Firefly and Firefly Algorithm, and Hybrid Cuckoo Search & Firefly Algorithm. These methods with a strong natural influence are crucial for applications involving distant sensing. These methods have shown good results showing increased efficiency and larger values of the Kappa coefficient, making them highly helpful for classifying aspects of the terrain. This research examines many methods of classifying satellite photos that draw inspiration from nature.

Underwater manipulator arm control based on Harris Hawk algorithm optimized RBF neural network

This article addresses the control issues of underwater manipulator arms in complex marine environments, proposing a composite control strategy based on the Harris Hawk Optimization (HHO) algorithm and Radial Basis Function (RBF) neural network. Combining the global search capability of the HHO algorithm with the fast approximation characteristics of RBF neural networks, a self-adaptive control method for underwater manipulator arms is designed. By automatically optimizing the parameters of the neural network, the performance and robustness of the control system are enhanced. Through simulation experiments, the effectiveness of the proposed control algorithm is verified. The results show that compared with the traditional RBF neural network control and fuzzy sliding mode control, the optimized control algorithm proposed in this paper has significant improvement compared with both of them, demonstrates good control effect and high practical value, and provides an effective solution for the precise control of the underwater manipulator arm.

Reinforcement-Learning-Based Multi-UAV Cooperative Search for Moving Targets in 3D Scenarios

Most existing multi-UAV collaborative search methods only consider scenarios of two-dimensional path planning or static target search. To be close to the practical scenario, this paper proposes a path planning method based on an action-mask-based multi-agent proximal policy optimization (AM-MAPPO) algorithm for multiple UAVs searching for moving targets in three-dimensional (3D) environments. In particular, a multi-UAV high–low altitude collaborative search architecture is introduced that not only takes into account the extensive detection range of high-altitude UAVs but also leverages the benefit of the superior detection quality of low-altitude UAVs. The optimization objective of the search task is to minimize the uncertainty of the search area while maximizing the number of captured moving targets. The path planning problem for moving target search in a 3D environment is formulated and addressed using the AM-MAPPO algorithm. The proposed method incorporates a state representation mechanism based on field-of-view encoding to handle dynamic changes in neural network input dimensions and develops a rule-based target capture mechanism and an action-mask-based collision avoidance mechanism to enhance the AM-MAPPO algorithm’s convergence speed. Experimental results demonstrate that the proposed algorithm significantly reduces regional uncertainty and increases the number of captured moving targets compared to other deep reinforcement learning methods. Ablation studies further indicate that the proposed action mask mechanism, target capture mechanism, and collision avoidance mechanism of the AM-MAPPO algorithm can improve the algorithm’s effectiveness, target capture capability, and UAVs’ safety, respectively.

Experimental, theoretical and optimization studies on multimode vibration reduction of floating raft system based on NES cells

Most of the studies on multimode vibration reduction of floating raft systems remain at the stage of theoretical analysis, while relevant experimental studies are rare. In this paper, an experimental setup for a scaled-down floating raft system is constructed. The vertical track nonlinear energy sink (TNES) cells are experimentally applied for the first time to suppress the multimode vibration of the floating raft system. To ensure the damping effect is not affected by additional mass, the TNES cells are always installed on the floating raft system. The TNES is able to achieve vibration suppression by allowing unrestricted movement of its vibrator. Two damping systems with different TNESs placement are proposed. Experimental modal analysis is performed on the floating raft structure to determine its response characteristics. Based on the results, different magnitudes of excitation force are applied to each order mode to effectively suppress the multimode vibration of the system. Meanwhile, the nonlinear system coupled with TNES cells is analyzed using the harmonic balance method (HBM) to obtain the approximate analytical solution. The system responses obtained from the analytical solution are compared with the experimental data results. The particle swarm optimization (PSO) algorithm is used to obtain the optimal parameters of the TNESs for vibration suppression. The results show that the multimodal vibration of the floating raft structure is effectively suppressed by the TNES cells in both experimental and theoretical analyses. The vibration suppression effect for all modes is significantly improved by the PSO algorithm. In conclusion, the research content of this paper provides a practical and feasible vibration suppression solution for the design of floating raft systems.

WVETT-Net: A Novel Hybrid Prediction Model for Wireless Network Traffic Based on Variational Mode Decomposition

Precise prediction of wireless communication network traffic is indispensable in the operational deployment of base station resources and improvement of the user experience. Cellular wireless network traffic has both spatial and temporal characteristics. The existing modeling algorithms have achieved good results in extracting the spatial features, but there are still deficiencies in the extraction models for the time dependencies. To resolve these problems, this paper proposes a novel hybrid neural network prediction model, called WVETT-Net. Firstly, variational mode decomposition (VMD) is used to preprocess network traffic, and the whale optimization algorithm (WOA) is used to select the optimal parameters for VMD. Secondly, the local and global features are extracted from each subsequence by a temporal convolutional network (TCN) and an improved Transformer network with a multi-head ProbSparse self-attention mechanism (Pe-Transformer), respectively. Finally, the extracted feature representation is enhanced by using an efficient channel attention (ECA) mechanism to achieve accurate wireless network traffic predictions. Experimental results on two wireless network traffic datasets show that the proposed model (WVETT-Net) outperforms the traditional single or combined models in wireless network traffic prediction.

A dual scale spatio‐temporal control for complex distributed parameter systems

AbstractIt is very difficult to control the distributed parameter system (DPS) for consistent spatial performance. In this paper, a dual scale spatio‐temporal control is designed for the DPS to achieve a consistent performance across the entire workspace under unknown exogenous disturbances. First, the generalized “spatial observer” should be designed under spectral decomposition/synthesis, to act as the nominal model of the DPS, through which the spatial mismatch between the model and the process could be estimated. Then a distributed disturbance compensator can be constructed to suppress all the undesirable spatial disturbance through the inner loop, and the compensated system will become closer to the nominal model and easier to control. Third, a dual controller will be designed in the outer loop for the final consistent spatial performance. Since the spatial performance is mainly affected by the dominant dynamics on the slow scale, a convex optimization algorithm can be effectively established in terms of nonlinear matrix inequalities for the dual controller. In this way, the nonlinear optimization problem is solved by converting it into the problem of a linear matrix inequalities with constraints. Besides, the spatio‐temporal state variable of the controlled plant is demonstrated to converge in Hilbert space. The feasibility and effectiveness of the proposed control method are verified by temperature control of a catalytic rod, a benchmark in chemical reactors.

RMOWOA: A Revamped Multi-Objective Whale Optimization Algorithm for Maximizing the Lifetime of a Network in Wireless Sensor Networks

RMOWOA: A Revamped Multi-Objective Whale Optimization Algorithm for Maximizing the Lifetime of a Network in Wireless Sensor Networks

Intelligent Health Care Data Analysis System Using Swarm based Optimization Algorithm

Large amounts of clinical data present both opportunities and challenges in modern healthcare. Effective data analysis improves patient care, early disease detection, and personalised treatment. This paper proposes an Intelligent Health Care Data Analysis System employing PSO and neural networks to improve disease detection. Missing values, normalisation, and feature selection ensure high-quality analysis inputs. Disease classification and prediction from clinical data are improved by PSO optimising neural network weights and biases. This method works on Hepatitis, Wisconsin Breast Cancer, and Cleveland Heart Disease datasets with and without missing values. PSO-optimized neural networks outperform regular neural networks in accuracy and robustness. Scalable and adaptable for real-time diagnostic applications, this intelligent system may improve healthcare delivery by enabling more precise and timely interventions. Using neural networks and advanced swarm-based optimisation, medical data analytics has improved patient outcomes and healthcare system efficiency. Particle Swarm Optimization-based Neural Network (IPSONN) diagnoses clinical illnesses better. IPSONN was created using K-means clustering and IPSO-trained Neural Networks. K-means clusterings optimal cluster number determines the hidden layers neurones. Cluster approximation error determines best clusters. Mean square error calculates hidden and output layer weights in QPSO. The classifier was tested with four clinical datasets from the University of California, Irvine (UCI) machine learning repository: Pima Indian Diabetes, Hepatitis, Wisconsin Breast Cancer, and Cleveland Heart Disease.

Stochastic Techno-Economic Optimization of Hybrid Energy System with Photovoltaic, Wind, and Hydrokinetic Resources Integrated with Electric and Thermal Storage Using Improved Fire Hawk Optimization

In this paper, a stochastic techno-economic optimization framework is proposed for three different hybrid energy systems that encompass photovoltaic (PV), wind turbine (WT), and hydrokinetic (HKT) energy sources, battery storage, combined heat and power generation, and thermal energy storage (Case I: PV–BA–CHP–TES, Case II: WT–BA–CHP–TES, and Case III: HKT–BA–CHP–TES), with the inclusion of electric and thermal storage using the 2m + 1 point estimate method (2m + 1 PEM) utilizing real data obtained from the city of Espoo, Finland. The objective function is defined as planning cost minimization. A new meta-heuristic optimization algorithm named improved fire hawk optimization (IFHO) based on the golden sine strategy is applied to find the optimal decision variables. The framework aims to determine the best configuration of the hybrid system, focusing on achieving the optimal size for resources and storage units to ensure efficient electricity and heat supply simultaneously with the lowest planning cost in different cases. Also, the impacts of the stochastic model incorporating the generation and load uncertainties using the 2m + 1 PEM are evaluated for different case results compared with the deterministic model without uncertainty. The results demonstrated that Case III obtained the best system configuration with the lowest planning cost in deterministic and stochastic models and. This case is capable of simply meeting the electrical and thermal load with the contribution of the energy resources, as well as the CHP and TESs. Also, the IFHO superiority is proved compared with the conventional FHO, and particle swarm optimization (PSO) achieves the lowest planning cost in all cases. Moreover, incorporating the stochastic optimization model, the planning costs of cases I–III are increased by 4.28%, 3.75%, and 3.57%, respectively, compared with the deterministic model. Therefore, the stochastic model is a reliable model due to its incorporating the existence of uncertainties in comparison with the deterministic model, which is based on uncertain data.