Optimization Algorithm Research Articles

A high-speed MPPT based horse herd optimization algorithm with dynamic linear active disturbance rejection control for PV battery charging system.

This study first proposes an innovative method for optimizing the maximum power extraction from photovoltaic (PV) systems during dynamic and static environmental conditions (DSEC) by applying the horse herd optimization algorithm (HHOA). The HHOA is a bio-inspired technique that mimics the motion cycles of an entire herd of horses. Next, the linear active disturbance rejection control (LADRC) was applied to monitor the HHOA's reference voltage output. The LADRC, known for managing uncertainties and disturbances, improves the anti-interference capacity of the maximum power point tracking (MPPT) technique and speeds up the system's response rate. Then, in comparison to the traditional method (perturb & observe; P&O) and metaheuristic algorithms (conventional particle swarm optimization; CPSO, grasshopper optimization; GHO, and deterministic PSO; DPSO) through DSEC, the simulations results demonstrate that the combination HHOA-LADRC can successfully track the global maximum peak (GMP) with less fluctuations and a quicker convergence time. Finally, the experimental investigation of the proposed HHOA-LADRC was accomplished with the NI PXIE-1071 Hardware-In-Loop (HIL) prototype. The output findings show that the effectiveness of the provided HHOA-LADRC may approach a value higher than 99%, showed a quicker rate of converging and less oscillations in power through the detection mechanism.

Metaheuristic algorithm for parametric optimization of liquid nitrogen pumping in hydrocarbon and allied fluids piping systems

Metaheuristic algorithm for parametric optimization of liquid nitrogen pumping in hydrocarbon and allied fluids piping systems

Optimization and bioapplication of deep learning algorithm in the prediction of mechanical properties of metal matrix composites

Optimization and bioapplication of deep learning algorithm in the prediction of mechanical properties of metal matrix composites

Modified particle swarm optimisation to determine the kinetic parameters of 2-chlorophenol oxidation in supercritical water

ABSTRACT An analytical chemical kinetics model can investigate the impact of concentrations, pressures, temperatures, and catalysts on the rates of reactions. It serves as the foundation for process design, effectiveness, and control. Nevertheless, the chemical kinetics model presents numerous dynamic characteristics that pose challenges in terms of prediction based on experimental empirical evidence. The present work employed three particle swarm optimisation (PSO) algorithms in order to determine the optimal parameters of the kinetic model. We wanted to reduce root mean squared errors. The operators’ efficacy is shown by numerical tests on benchmark functions and comparison to the fundamental GWO and ABC operators. The computational findings demonstrate that m-PSO shows a least RMSE value of 0.043 in comparison to other models and also significantly enhances both accuracy and convergence rate compared to other described methods. The model's improved search capabilities are shown by the kinetic parameter estimate findings utilising supercritical water oxidation experimental data.

LEVYEFO-WTMTOA: the hybrid of the multi-tracker optimization algorithm and the electromagnetic field optimization

LEVYEFO-WTMTOA: the hybrid of the multi-tracker optimization algorithm and the electromagnetic field optimization

Predicting and synthesizing terahertz spoof surface plasmon polariton devices with a convolutional neural network model.

With the increasing global attention to deep learning and the advancements made in applying convolutional neural networks in electromagnetics, we have recently witnessed the utilization of deep learning-based networks for predicting the spectrum and electromagnetic properties of structures instead of traditional tools like fully numerical-based methods. In this study, a Convolutional Neural Network (CNN is proposed for predicting spoof surface plasmon polaritons, enabling the examination of the absorption spectrum of metallic multilevel-grating structures (MMGS) and designing various sensor devices and absorbers in the shortest time possible. To expedite the training process of this network, a semi-analytical method of rigorous coupled-wave analysis (RCWA) enhanced with the fast Fourier factorization (FFF) technique has been employed, significantly reducing the data generation time for training. This CNN enables the prediction of existing spoof surface plasmon polaritons (SSPPs) and their intensity within a 110% frequency range. In addition to the speed of dataset generation, the simple framework of this network has also facilitated the training of the network in a short time. By comparing the network in this study with previous works, it is apparent that in addition to structural and geometrical changes in the unit cell, the designer is afforded greater freedom in determining the material of the incident medium and, for the first time, specifying the angle of incidence of the source. Finally, for the validation of the suggested network, the predictive power of the absorption spectrum of various structures is compared with traditional methods. Three examples are provided for inversely designing several sensor devices and absorbers in the terahertz band using the proposed CNN and the genetic optimization algorithm.

Systematic Literature Review of Optimization Algorithms for PCmax Problem||

Systematic Literature Review of Optimization Algorithms for PCmax Problem||

Neural Network Prediction of Locomotive Engine Parameters Based on the Dung Beetle Optimization Algorithm and Multi-Objective Optimization of Engine Operating Parameters

Neural Network Prediction of Locomotive Engine Parameters Based on the Dung Beetle Optimization Algorithm and Multi-Objective Optimization of Engine Operating Parameters

Developing picking route policies with genetic algorithms and order batching with deep neural networks in picker to part warehouses

ABSTRACT This paper proposes a hybrid approach to warehouse management, combining Genetic Algorithm (GA) optimization for picking routes with Deep Neural Network (DNN) classification for order batching. The GA-DNN method significantly reduces travel distances while lowering computational time. Experimental results demonstrate up to 69.01% reduction in travel distance in large warehouses, outperforming traditional methods. This study underscores the effectiveness of GA-DNN solutions in addressing modern logistics challenges.

Integrating novel stellarator single-stage optimization algorithms to design the Columbia stellarator experiment

Abstract The Columbia Stellarator eXperiment (CSX), currently being designed at Columbia University, aims to test theoretical predictions related to QA plasma behavior, and to pioneer the construction of an optimized stellarator using three-dimensional, non-insulated high-temperature superconducting (NI-HTS) coils. The magnetic configuration is generated by a combination of two circular planar poloidal field (PF) coils and two 3D-shaped interlinked (IL) coils, with the possibility to add windowpane coils to enhance shaping and experimental flexibility. The PF coils and vacuum vessel are repurposed from the former Columbia Non-Neutral Torus (CNT) experiment, while the IL coils will be custom-wound in-house using NI-HTS tapes. To obtain a plasma shape that meets the physics objectives with a limited number of coils, novel single-stage optimization techniques are employed, optimizing both the plasma and coils concurrently, in particular targeting a tight aspect ratio QA plasma and minimized strain on the HTS tape. Despite the increased complexity due to the expanded degrees of freedom, these methods successfully identify optimized plasma geometries that can be realized by coils meeting engineering specifications. This paper discusses the derivation of the constraints and objectives specific to CSX, and describe how two recently developed single-stage optimization methodologies are applied to the design of CSX. A set of selected configurations for CSX is then described in detail.

Quantum distance approximation for persistence diagrams

Abstract Topological Data Analysis methods can be useful for classification and clustering
tasks in many different fields as they can provide two dimensional persistence dia-
grams that summarize important information about the shape of potentially complex
and high dimensional data sets. The space of persistence diagrams can be endowed
with various metrics, which admit a statistical structure and allow to use these summaries for machine learning algorithms, e.g. the Wasserstein distance.

However, computing the distance between two persistence diagrams involves finding an opti-
mal way to match the points of the two diagrams and may not always be an easy
task for classical computers. Recently, quantum algorithms have shown the po-
tential to speedup the process of obtaining the persistence information displayed on
persistence diagrams by estimating the spectra of persistent Dirac operators. So,
in this work we explore the potential of quantum computers to estimate the distance
between persistence diagrams as the next step in the design of a fully quantum frame-
work for TDA. In particular we propose variational quantum algorithms for the
Wasserstein distance as well as the dcp distance. Our implementation is a weighted
version of the Quantum Approximate Optimization Algorithm that relies on control
clauses to encode the constraints of the optimization problem.

A Markov decision optimization of medical service resources for two-class patient queues in emergency departments via particle swarm optimization algorithm.

In the modern healthcare system, the rational allocation of emergency department (ED) resources is crucial for enhancing emergency response efficiency, ensuring patient safety, and improving the quality of medical services. This paper focuses on the issue of ED resource allocation and designs a priority sorting system for ED patients. The system classifies patients into two queues: urgent and routine. Considering different service rates, a multi-server preemptive priority queueing model [Formula: see text] and a multi-server non-preemptive priority queueing model [Formula: see text] are constructed. Additionally, the number of beds, K, is introduced as the capacity of the urgent queue. By comprehensively considering the costs associated with patient waiting time, the cost of rejecting the most critical patients, and the total costs of beds and servers, a mixed-integer programming model was constructed with the objective of minimizing the total cost. The particle swarm optimization algorithm was applied to determine the optimal number of servers, service rate, and number of beds. Compared with the model proposed by Alipour-Vaezi et al., our model significantly improves patient waiting times and queue lengths using the same data set: the waiting time [Formula: see text] decreased by 74.44%, [Formula: see text] by 5.79%, and [Formula: see text] by 1.13%; the queue length [Formula: see text] decreased by 78% and [Formula: see text] by 3.33%. Our model effectively reduces patient waiting times and queue lengths while controlling costs, identifies the optimal number of beds, and achieves optimized resource allocation. Finally, we conducted a sensitivity analysis and provided some valuable management insights.

A Study of the Stability of an Industrial Robot Servo System: PID Control Based on a Hybrid Sparrow Optimization Algorithm

A Study of the Stability of an Industrial Robot Servo System: PID Control Based on a Hybrid Sparrow Optimization Algorithm

Swin-transformer for weak feature matching.

Feature matching in computer vision is crucial but challenging in weakly textured scenes due to the lack of pattern repetition. We introduce the SwinMatcher feature matching method, aimed at addressing the issues of low matching quantity and poor matching precision in weakly textured scenes. Given the inherently significant local characteristics of image features, we employ a local self-attention mechanism to learn from weakly textured areas, maximally preserving the features of weak textures. To address the issue of incorrect matches in scenes with repetitive patterns, we use a cross-attention and positional encoding mechanism to learn the correct matches of repetitive patterns in two scenes, achieving higher matching precision. We also introduce a matching optimization algorithm that calculates the spatial expected coordinates of local two-dimensional heat maps of correspondences to obtain the final sub-pixel level matches. Experiments indicate that, under identical training conditions, the SwinMatcher outperforms other standard methods in pose estimation, homography estimation, and visual localization. It exhibits strong robustness and superior matching in weakly textured areas, offering a new research direction for feature matching in weakly textured images.

Automatic visual detection of activated sludge microorganisms based on microscopic phase contrast image optimisation and deep learning.

The types and quantities of microorganisms in activated sludge are directly related to the stability and efficiency of sewage treatment systems. This paper proposes a sludge microorganism detection method based on microscopic phase contrast image optimisation and deep learning. Firstly, a dataset containing eight types of microorganisms is constructed, and an augmentation strategy based on single and multisamples processing is designed to address the issues of sample deficiency and uneven distribution. Secondly, a phase contrast image quality optimisation algorithm based on fused variance is proposed, which can effectively improve the standard deviation, entropy, and detection performance. Thirdly, a lightweight YOLOv8n-SimAM model is designed, which introduces a SimAM attention module to suppress the complex background interference and enhance attentions to the target objects. The lightweight of the network is realised using a detection head based on multiscale information fusion convolutional module. In addition, a new loss function IW-IoU is proposed to improve the generalisation ability and overall performance. Comparative and ablative experiments are conducted, demonstrating the great application potential for rapid and accurate detection of microbial targets. Compared to the baseline model, the proposed method improves the detection accuracy by 12.35% and hastens the running speed by 37.9 frames per second while evidently reducing the model size.

A quasi-opposition learning and chaos local search based on walrus optimization for global optimization problems

The Walrus Optimization (WO) algorithm, as an emerging metaheuristic algorithm, has shown excellent performance in problem-solving, however it still faces issues such as slow convergence and susceptibility to getting trapped in local optima. To this end, the study proposes a novel WO enhanced by quasi-oppositional-based learning and chaotic local search mechanisms, called QOCWO. The study aims to prevent premature convergence to local optima and enhance the diversity of the population by integrating the quasi-oppositional-based learning mechanism into the original Walrus Optimization (WO) algorithm, thereby improving the global search capability and expanding the search range. Additionally, the chaotic local search mechanism is introduced to accelerate the convergence speed of WO. To test the capabilities, the QOCWO algorithm is applied to the 23 standard functions and compared with seven other algorithms. Furthermore, the Wilcoxon rank-sum test is utilized to evaluate the significance of the results, which demonstrates the superior performance of the proposed algorithm. To assess the practicality in solving real-world problems, the QOCWO is applied to two engineering design issues, and the results indicated that QOCWO achieved lower costs compared to other algorithms.

Application of improved multi-population genetic algorithm in structural optimization of automotive electrical equipment

Application of improved multi-population genetic algorithm in structural optimization of automotive electrical equipment

Efficient Workflow Scheduling in Cloud Computing Using Hybrid Algorithm

In recent years, cloud computing has increasingly embraced a pay-per-use model, offering dynamic, virtualized resources via the internet. The central challenge in this domain is efficiently scheduling workflows, considering deadlines and budgets while optimizing task allocation to virtual machines (VMs). Our study hypothesizes that improved scheduling can reduce energy consumption, streamline process execution, and lower operational costs. To test this hypothesis, we conducted a comparative analysis of two optimization techniques: Genetic Algorithm with Multiple Particle Swarm Optimization (GA + MPSO) and Genetic Algorithm with Bat Algorithm (GA + BAT). The analysis reveals that the combination of Genetic Algorithm and Bat Algorithm (GA + BAT) excels in optimizing cloud computing workflow scheduling. GA + BAT demonstrated superior performance by significantly reducing energy consumption, shortening process execution times, and decreasing operational costs. These findings validate our hypothesis, underscoring that optimizing cloud computing workflow scheduling can deliver substantial benefits. Consequently, by adopting GA + BAT, cloud service providers can enhance efficiency, reduce costs, and foster a more sustainable and responsive cloud infrastructure.

A new method for recognizing geometric parameters of industrial robots

Intelligent algorithms that are commonly used to obtain errors in the geometric parameters of industrial robots have a low accuracy, easily fall into the local optimal solution, and involve complicated coding such that they are unsuitable for use in engineering. In this study, we first apply the D-H method to establish a model of error in industrial robots, and then use the set of errors in their geometric parameters as the objective function. Following this, we improve the accuracy of global optimization of the particle swarm optimization (PSO) algorithm by drawing on the wandering behavior of the wolf pack algorithm and hybridization behavior of the genetic algorithm. We balance the convergence of the PSO algorithm by using a linearly diminishing weight. This leads to an improved PSO algorithm that can accurately determine errors in the geometric parameters of industrial robots. We compared our improve PSO algorithm with commonly used particle swarm algorithms, and the results showed that the former had a higher accuracy of convergence on average. Moreover, the errors in the geometric parameters obtained by the improved PSO algorithm can enhance the accuracy of localization of errors in industrial robots.

Enhancing colour image encryption through parameters optimization of memristive hyperchaotic system with CPSO algorithm and LSAIM

ABSTRACT Particle Swarm Optimization (PSO) is a simple and effective algorithm for optimization. However, it often converges too early and depends heavily on proper parameter settings for good results, especially in complex or high-dimensional search spaces. Therefore, this algorithm is inappropriate for parameter optimization of hyperchaotic systems and may not provide sufficient robustness and security of colour images. This study presents a new colour image encryption based on optimizing a four-dimensional Memristor-based hyperchaotic system, in which the fourteen parameters of the hyperchaotic system are optimized by the Chaotic Particle Swarm Optimization (CPSO) method, in combination with DNA code and a new logistic sine adjusted integrated map (LSAIM). Simulation results have shown that the algorithm achieved a key space of up to 21116, providing sufficient protection against brute-force attacks, with entropy values close to the ideal (7.9994), NPCR of 99.6178%, and UACI of 33.965%, indicating robust security.