Optimization Algorithm Research Articles

Tuning the activation function to optimize the forecast horizon of a reservoir computer

Reservoir computing is a machine learning framework where the readouts from a nonlinear system (the reservoir) are trained so that the output from the reservoir, when forced with an input signal, reproduces a desired output signal. A common implementation of reservoir computers (RCs) is to use a recurrent neural network as the reservoir. The design of this network can have significant effects on the performance of the RC. In this paper we study the effect of the node activation function on the ability of RCs to learn and predict chaotic time series. We find that the Forecast Horizon (FH), the time during which the reservoir’s predictions remain accurate, can vary by an order of magnitude across a set of 16 activation functions used in machine learning. By using different functions from this set, and by modifying their parameters, we explore whether the entropy of node activation levels or the curvature of the activation functions determine the predictive ability of the reservoirs. We find that the FH is low when the activation function is used in a region where it has low curvature, and a positive correlation between curvature and FH. For the activation functions studied we find that the largest FH generally occurs at intermediate levels of the entropy of node activation levels. Our results show that the performance of RCs is very sensitive to the activation function shape. Therefore, modifying this shape in hyperparameter optimization algorithms can lead to improvements in RC performance.

Multi-dimensional early warning of the entire supply chain of power materials based on RFID technology

Early warning system needs to process a large amount of real-time data, and carry out in-depth analysis and mining of these data to identify potential risks and hidden dangers. However, existing data processing and analysis capabilities may not be able to meet this demand. To this end, a multi-dimensional early warning of the entire supply chain of power materials based on RFID technology is designed. According to the real-time failure probability of power materials, the probability of early warning accidents is calculated and classified, and the risk is graded based on these probabilities. Through the methods of bonus points, deduction points and grade evaluation, the risk early warning indicators of different stages in the whole supply chain of power materials were quantified. The objective and rationality of risk assessment can be ensured by means of comprehensive weight. A multi-dimensional early warning system based on RFID technology is established, combining multiple linear regression model and particle swarm optimization algorithm to determine the time window of multi-dimensional early warning, and carry out dynamic monitoring and early warning of supply chain. The experimental results show that the early warning effect of the design method can reach 95% and the highest early warning effect can reach 98% at 10 seconds. The average warning error is only 2.91%, and the average warning time is only 1.34 seconds, which is more accurate in identifying the number of first-level risks, second-level risks and third-level risks.

Optimized multi-head self-attention and gated-dilated convolutional neural network for quantum key distribution and error rate reduction

ABSTRACT Quantum key distribution (QKD) is a secure communication method that enables two parties to securely exchange a secret key. The secure key rate is a crucial metric for assessing the efficiency and practical viability of a QKD system. There are several approaches that are utilized in practice to calculate the secure key rate. In this manuscript, QKD and error rate optimization based on optimized multi-head self-attention and gated-dilated convolutional neural network (QKD-ERO-MSGCNN) is proposed. Initially, the input signals are gathered from 6G wireless networks which face obstacles to channel. For extending maximum transmission distances and improving secret key rates, the signals are fed to the variable velocity strategy particle swarm optimization algorithm, then the signals are fed to MSGCNN for analysing the quantum bit error rate reduction. The MSGCNN is optimized by intensified sand cat swarm optimization. The performance of the QKD-ERO-MSGCNN approach attains 15.57%, 23.89%, and 31.75% higher accuracy when analysed with existing techniques, like device-independent QKD utilizing random quantum states, practical continuous-variable QKD and feasible optimization parameters, entanglement and teleportation in QKD for secure wireless systems, and QKD for large scale networks methods, respectively.

Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies

Parameter Identification of Photovoltaic Models Using Enhanced Crayfish Optimization Algorithm with Opposition-Based Learning Strategies

Soft-thresholding Alternating Minimization Optimization Algorithm for Turntable-free Calibration Based on the Miniature Inertial Measurement Unit

Soft-thresholding Alternating Minimization Optimization Algorithm for Turntable-free Calibration Based on the Miniature Inertial Measurement Unit

Efficient maximum power point extraction using hybrid optimisation based parameter estimation model of solar double diode model

ABSTRACT PV systems operate most efficiently when they are operated at their Maximum Power Point (MPP). Despite the wide range of MPPT approaches available, there is potential to improve the efficiency of MPP Tracking (MPPT). One method to achieve this improvement is to use model-based MPPT techniques, which can speed up the MPP search process by estimating the MPP with a high degree of accuracy. The main objective of the research, which is to create an efficient double-diode model-based MPPT technique with a focus on hybrid Optimisation, is informed by this justification. The suggested method for predicting the MPP voltage makes use of an accurate double-diode model for PV modules that is dependent on temperature and irradiance. This research employs a parameter estimation system based on the proposed Optimisation algorithm known as Memory Integrated Sand Cat Swarm Optimisation Algorithm (MI-SCS) to overcome the limitations of standard parameter estimate methods, which are related to the availability of experimental p-V curves or datasheets with precise temperature and irradiance values. The method that is being given is based on the integration of the Crow Search Optimisation (CSO) Algorithm with the Sand Cat Swarm Optimisation (SCSO) algorithm.

Efficient maximum power point extraction using hybrid optimisation based parameter estimation model of solar double diode model

ABSTRACT PV systems operate most efficiently when they are operated at their Maximum Power Point (MPP). Despite the wide range of MPPT approaches available, there is potential to improve the efficiency of MPP Tracking (MPPT). One method to achieve this improvement is to use model-based MPPT techniques, which can speed up the MPP search process by estimating the MPP with a high degree of accuracy. The main objective of the research, which is to create an efficient double-diode model-based MPPT technique with a focus on hybrid Optimisation, is informed by this justification. The suggested method for predicting the MPP voltage makes use of an accurate double-diode model for PV modules that is dependent on temperature and irradiance. This research employs a parameter estimation system based on the proposed Optimisation algorithm known as Memory Integrated Sand Cat Swarm Optimisation Algorithm (MI-SCS) to overcome the limitations of standard parameter estimate methods, which are related to the availability of experimental p-V curves or datasheets with precise temperature and irradiance values. The method that is being given is based on the integration of the Crow Search Optimisation (CSO) Algorithm with the Sand Cat Swarm Optimisation (SCSO) algorithm.

Hybrid YSGOA and neural networks based software failure prediction in cloud systems

In the realm of cloud computing, ensuring the dependability and robustness of software systems is paramount. The intricate and evolving nature of cloud infrastructures, however, presents substantial obstacles in the pre-emptive identification and rectification of software anomalies. This study introduces an innovative methodology that amalgamates hybrid optimization algorithms with Neural Networks (NN) to refine the prediction of software malfunctions. The core objective is to augment the purity metric of our method across diverse operational conditions. This is accomplished through the utilization of two distinct optimization algorithms: the Yellow Saddle Goat Fish Algorithm (YSGA), which is instrumental in the discernment of pivotal features linked to software failures, and the Grasshopper Optimization Algorithm (GOA), which further polishes the feature compilation. These features are then processed by Neural Networks (NN), capitalizing on their proficiency in deciphering intricate data patterns and interconnections. The NNs are integral to the classification of instances predicated on the ascertained features. Our evaluation, conducted using the Failure-Dataset-OpenStack database and MATLAB Software, demonstrates that the hybrid optimization strategy employed for feature selection significantly curtails complexity and expedites processing

Multi-headed U-Net: an automated nuclei segmentation technique using Tikhonov filter-based unsharp masking

ABSTRACT An automated nuclei segmentation is the key technique for understanding and analyzing cellular properties, which are helpful for disease diagnosis and support computer-aided digital pathology. However, this task is challenging because of the variability in nuclei size and morphology, which results in blurry boundaries and overlapping nuclei. To address such issues, a multi-headed U-Net convolutional neural network (CNN) architecture has been proposed. This architecture has multiple heads to extract multi-resolution features of the source image by using different kernel sizes of the filters. The source images are pre-processed using an unsharp masking approach based on the Tikhonov filter. The Tikhonov filter decomposes the input image into low-frequency and high-frequency band images. The unsharp masking method improves the high-frequency information of the input image by primarily enhancing features such as boundaries, contours, and fine details. We have incorporated intersection over union (IOU) and F1Score as measures along with accuracy for our proposed objective functions. The proposed objective functions are tried to be maximized by the optimization algorithm, and the higher value of the metrics indicates better segmentation performance in the spatial domain during the testing phase. The proposed method attained IOU(JI), Accuracy, Precision, and F1Score values as 0.8299, 0.9642, 0.8918, and 0.9070, respectively. The quantitative and qualitative experimental outcomes indicate that our proposed technique outperforms the state-of-the-art techniques.

Multi-headed U-Net: an automated nuclei segmentation technique using Tikhonov filter-based unsharp masking

ABSTRACT An automated nuclei segmentation is the key technique for understanding and analyzing cellular properties, which are helpful for disease diagnosis and support computer-aided digital pathology. However, this task is challenging because of the variability in nuclei size and morphology, which results in blurry boundaries and overlapping nuclei. To address such issues, a multi-headed U-Net convolutional neural network (CNN) architecture has been proposed. This architecture has multiple heads to extract multi-resolution features of the source image by using different kernel sizes of the filters. The source images are pre-processed using an unsharp masking approach based on the Tikhonov filter. The Tikhonov filter decomposes the input image into low-frequency and high-frequency band images. The unsharp masking method improves the high-frequency information of the input image by primarily enhancing features such as boundaries, contours, and fine details. We have incorporated intersection over union (IOU) and F1Score as measures along with accuracy for our proposed objective functions. The proposed objective functions are tried to be maximized by the optimization algorithm, and the higher value of the metrics indicates better segmentation performance in the spatial domain during the testing phase. The proposed method attained IOU(JI), Accuracy, Precision, and F1Score values as 0.8299, 0.9642, 0.8918, and 0.9070, respectively. The quantitative and qualitative experimental outcomes indicate that our proposed technique outperforms the state-of-the-art techniques.

High-order complex Fourier numerical manifold method for improving the optimization of cracked structures

In this paper, a combination of the high-order numerical manifold method with material interpolation is established with the goal of improving the optimization of cracked structures. Complex Fourier shape functions, known for their inherent advantages, are utilized as weight functions in the numerical manifold method. By implementing high-order analysis, the occurrence of checkerboard patterns is effectively avoided. In addition, a modified sensitivity filtering technique is introduced to address the issue of mesh dependency and minimum length scale in the final topology. This technique is based on the notion of a manifold element. Furthermore, a methodology is introduced to represent void regions within the design domain without the need for passive elements. This methodology involves describing an algorithm to accurately determine the manifold element and the corresponding element area within the final topology. Numerical problems of topology optimization are presented to examine all the aforementioned advantages. The findings indicate that the proposed algorithm for topology optimization does not exhibit any instances of checkerboard patterns. Moreover, the final topology of the curved void regions does not show any zigzag patterns. The above method demonstrates a commendable rate of convergence, and the final topology of the cracked regions can be conveniently simulated.

Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction.

Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R2), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R2 values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data.

Layered learning in a quadrotor drone: Simultaneous controlling and path planning using optimal fuzzy fractional order proportional integral derivative and proximal policy optimization

Unmanned aerial vehicles (UAVs), particularly quadrotors, have seen a surge in applications ranging from surveillance to delivery services. However, autonomous control remains a challenge due to the complexity of their dynamics and the need for real-time responsiveness. This paper addresses the significant problem of autonomous quadrotor control by introducing a novel learning method that integrates a deep neural network with advanced control techniques. Novelty of the work is distribution of different learning tasks in different layers of learning and combining the total learning system in a applicable UAV. The purpose is to enhance the quadrotor’s ability to perform complex maneuvers autonomously, which is crucial for expanding its application scope. The proposed method begins with the acquisition of direction control using a fractional order proportional–integral–derivative (FOPID) controller. The learning parameters are refined through a fuzzy method, ensuring adaptability and precision. For the optimization of the FOPID parameters, we apply two stochastic algorithms: Genetic Algorithm and Particle Swarm Optimization, comparing their efficacy. Our approach successfully teaches the quadrotor to generate and control a flight path autonomously. The second stage of learning involves proximal policy optimization, enabling the execution of intricate maneuvers. The experimental setup includes a quadrotor on a test stand with three degrees of freedom, where we validate the method through rigorous simulation and physical tests. The results demonstrate that our method not only teaches the quadrotor complex maneuvers but also ensures robust and precise execution, marking a significant advancement in UAV control systems. The implications of this research extend to improving UAV reliability for critical applications, potentially transforming the landscape of autonomous aerial operations.

Artificial neural networks (ANN)-genetic algorithm (GA) optimization on thermosonicated achocha juice: kinetic and thermodynamic perspectives of retained phytocompounds

The extraction of phytocompounds from Achocha (Cyclanthera pedata) vegetable juice using traditional methods often results in suboptimal yields and efficiency. This study aimed to enhance the extraction process through the application of thermosonication (TS). To achieve this, an artificial neural network (ANN) and a genetic algorithm (GA) were utilized to simulate and optimize the process parameters. The study investigated the influence of ultrasonic amplitude (30%–50%), temperature (30 °C–50 °C), and sonication duration (15–60 min) on total polyphenolic content (TPC), total flavonoid content (TFC), antioxidant activity (AOA), and ascorbic acid content (AA). Remarkably, the ANN-GA optimization resulted in optimal TS conditions: an ultrasonic amplitude of 40%, a temperature of 40 °C, and a sonication duration of 30 min. Subsequent analysis of extraction kinetics and thermodynamics across various temperatures (30 °C–50 °C) and extraction times (0–30 min) demonstrated R 2 (0.98821) and χ2 (1.74773) for TPC with activation energy (Ea) 26.0456, R 2 (0.99906) and χ2 (0.07215) for TFC with Ea 26.2336, R 2 (0.99867) and χ2 (0.03003) for AOA with Ea 26.0987, R 2 (0.99731) and χ2 (0.13719) for AA with Ea 26.0984, validating the pseudo second-order kinetic model. These findings indicate that increased temperature enhances the saturation concentration and rate constant of phytochemical extraction.

Multi-objective optimization algorithm for multi-workflow computation offloading in resource-limited IIoT

Industrial internet of things (IIoT) connects traditional industrial devices with the network to provide intelligent services, which is regarded as the key technology for achieving Industry 4.0 and enabling the transformation of the manufacturing sector. Multi-access edge computing (MEC) has brought significant opportunities to expedite the development of IIoT. However, the unique task characteristics and dense deployment of IIoT devices, coupled with the resource starvation problem (RSP) arising from the limited resources of edge servers, pose challenges to the direct applicability of existing MEC algorithms in MEC-assisted IIoT scenarios. To this end, a multi-objective evolutionary algorithm is proposed to simultaneously optimize delay and energy consumption for multi-workflow execution in resource-limited IIoT. First, the initialization of execution location based on delay and the initialization of execution order satisfying the priority constraint can generate high-quality initial solutions. Then, the improved crossover and mutation operations guide the population evolution, which can span the large infeasible solution region. Finally, dynamic task scheduling (DTS) dynamically changes the execution location of tasks affected by RSP according to the execution efficiency, so as to avoid the tasks blindly waiting for server resources. The comprehensive simulation results demonstrate the effectiveness of the proposed method in achieving a balance between the execution delay and energy consumption of IIoT devices.

Simultaneous thermal camouflage and radiative cooling for ultrahigh-temperature objects using inversely designed hierarchical metamaterial

Abstract Sophisticated infrared detection technology, operating through atmospheric transmission windows (usually between 3 and 5 μm and 8–13 μm), can detect an object by capturing its emitted thermal radiation, posing a threat to the survival of targeted objects. As per Wien’s displacement law, the shift of peak wavelength towards shorter wavelengths as blackbody temperature rises, underscores the significance of the 3–5 μm range for ultra-high temperature objects (e.g., at 400 °C), emphasizing the crucial need to control this radiation for the objects’ viability. Additionally, effective heat management is essential for ensuring the consistent operation of these ultrahot entities. In this study, based on a database with high-temperature resist materials, we introduced a material-informatics-based framework aimed at achieving the inverse design of simultaneous thermal camouflage (low emittance in the 3–5 μm range) and radiative cooling (high emittance in the non-atmospheric window 5–8 μm range) tailored for ultrahigh-temperature objects. Utilizing the transfer matrix method to calculate spectral properties and employing the particle swarm optimization algorithm, two optimized multilayer structures with desired spectral characteristics are obtained. The resulted structures demonstrate effective infrared camouflage at temperatures up to 250 °C and 500 °C, achieving reductions of 86.7 % and 63.7 % in the infrared signal, respectively. At equivalent heating power densities applied to the structure and aluminum, structure 1 demonstrates a temperature reduction of 29.4 °C at 0.75 W/cm2, while structure 2 attains a temperature reduction of 57.5 °C at 1.50 W/cm2 compared to aluminum, showcasing enhanced radiative cooling effects. This approach paves the way for attenuating infrared signals from ultrahigh-temperature objects and effectively managing their thermal conditions.

Spacecraft Anti-reconnaissance Game based on Particle Swarm Optimization Algorithm

Spacecraft Anti-reconnaissance Game based on Particle Swarm Optimization Algorithm

Generalization of Bernoulli polynomials to find optimal solution of fractional hematopoietic stem cells model

The study introduces a fractional mathematical model in the Caputo sense for hematopoietic stem cell-based therapy, utilizing generalized Bernoulli polynomials (GBPs) and operational matrices to solve a system of nonlinear equations. The significance of the study lies in the potential therapeutic applications of hematopoietic stem cells (HSCs), particularly in the context of HIV infection treatment, and the innovative use of GBPs and Lagrange multipliers in solving the fractional hematopoietic stem cells model (FHSCM). The aim of the study is to introduce an optimization algorithm for approximating the solution of the FHSCM using GBPs and Lagrange multipliers and to provide a comprehensive exploration of the mathematical techniques employed in this context. The research methodology involves formulating operational matrices for fractional derivatives of GBPs, conducting a convergence analysis of the proposed method, and demonstrating the accuracy of the method through numerical simulations. The major conclusion is the successful introduction of GBPs in the context of the FHSCM, featuring innovative control parameters and a novel optimization technique. The study also highlights the significance of the proposed method in providing accurate solutions for the FHSCM, thus contributing to the field of mathematical modeling in biological and medical research.

Joint risk-based maintenance strategy and integrated monitoring approach for manufacturing system operational process considering workpiece quality

The fluctuation of various parameters in the production process increases the risk of generating unqualified products and suffering from systematic failures and downtimes, and the preventive maintenance and control of potentially faulty machines in manufacturing systems should consider the continuous increase of operational risk. Therefore, to proactive control the manufacturing system operational risk, a joint model of risk-based maintenance (RBM) and integrated monitoring is proposed. The proposed model formulates an appropriate maintenance strategy and appropriate control charts for monitoring the manufacturing process, guaranteeing the stable operation of the manufacturing system. First, considering the co-effect of the production equipment state and workpiece quality, a novel connotation of the manufacturing system mechanism’s operational risk is proposed, and the RBM framework is constructed. Second, considering the manufacturing system operational state, an integrated maintenance decision-making model based on different risk scenarios is established using a dynamic monitoring scheme based on EWMA control charts. Third, a particle swarm optimisation algorithm is adopted to optimise the proposed model to obtain the optimal maintenance strategy for production equipment and monitoring parameter for workpiece quality. Finally, the effectiveness of the proposed approach is illustrated with a case study on a subway current receiver production line.

A Multi-strategy Improved Grasshopper Optimization Algorithm for Solving Global Optimization and Engineering Problems

This paper presents a multi-strategy improved grasshopper optimization algorithm (MSIGOA), which aims to address the shortcomings of the grasshopper optimization algorithm (GOA), including its slow convergence, vulnerability to trapping into local optima, and low accuracy. Firstly, to improve the uniformity of the population distribution in the search space, the MSIGOA uses circle mapping for the population initialization. A nonlinear decreasing coefficient is utilized instead of an original linear decreasing coefficient to improve the local exploitation and global exploration capabilities. Then, the modified golden sine mechanism is added during the position update stage to change the single position update mode of GOA and enhance the local exploitation capability. The greedy strategy is added to greedily select the new and old positions of the individual to retain a better position and increase the speed of convergence. Finally, the quasi-reflection-based learning mechanism is utilized to construct new populations to improve population multiplicity and the capability to escape from the local optima. This paper verifies the efficacy of MSIGOA by comparing it with other advanced algorithms on six engineering design problems, CEC2017 test functions, and 12 classical benchmark functions. The experimental results show that MSIGOA performs better than the original GOA and other compared algorithms and has stronger comprehensive optimization capabilities.