Search Algorithm Research Articles

Hybridized grasshopper optimization and cuckoo search algorithm for the classification of malware

Hybridized grasshopper optimization and cuckoo search algorithm for the classification of malware

Permanent magnet synchronous motor demagnetization fault diagnosis based on PCA-ISSA-PNN

Aiming at the demagnetization fault problem of the permanent magnet synchronous motor (PMSM), a demagnetization fault diagnosis method based on the combination of the principal component analysis (PCA) algorithm, the improved sparrow search algorithm (ISSA), and the probabilistic neural network (PNN) algorithm is proposed. First, the principal components of phase currents are extracted using PCA. Second, ISSA is used to optimize the smoothing coefficients of the PNN algorithm, and the optimized PNN algorithm is combined with PCA to obtain the PCA-ISSA-PNN fault diagnosis model. Finally, the established fault diagnosis model was tested using the current data collected from the experiments and compared with the fault diagnosis indexes and optimization performance of the conventional PNN, PCA-PNN, PCA-GA (genetic algorithm)-PNN, PCA-DA (dragonfly algorithm)-PNN, PCA-GTO (artificial gorilla troop optimizer)-PNN, PCA-AHA-PNN, and PCA-SSA-PNN. The test results show that the fault diagnosis accuracy of PCA-ISSA-PNN reaches 95.83%, and the fault diagnosis indexes are significantly higher than those of PNN, PCA-PNN, PCA-GA-PNN, and PCA-DA-PNN; its optimization performance is also significantly better than that of PCA-GTO-PNN, PCA-AHA-PNN, and PCA-SSA-PNN, which verifies the accuracy and efficiency of the proposed method.

Estimating power in complex nonlinear structural equation modeling including moderation effects: The powerNLSEM R-package

AbstractThe model-implied simulation-based power estimation (MSPE) approach is a new general method for power estimation (Irmer et al., 2024). MSPE was developed especially for power estimation of non-linear structural equation models (SEM), but it also can be applied to linear SEM and manifest models using the package . After first providing some information about MSPE and the new adaptive algorithm that automatically selects sample sizes for the best prediction of power using simulation, a tutorial on how to conduct the MSPE for quadratic and interaction SEM (QISEM) using the package is provided. Power estimation is demonstrated for four methods, latent moderated structural equations (LMS), the unconstrained product indicator (UPI), a simple factor score regression (FSR), and a scale regression (SR) approach to QISEM. In two simulation studies, we highlight the performance of the MSPE for all four methods applied to two QISEM with varying complexity and reliability. Further, we justify the settings of the newly developed adaptive search algorithm via performance evaluations using simulation. Overall, the MSPE using the adaptive approach performs well in terms of bias and Type I error rates.

Path Planning for Wall-Climbing Robots Using an Improved Sparrow Search Algorithm

Path Planning for Wall-Climbing Robots Using an Improved Sparrow Search Algorithm

Enhancing Target Tracking: A Novel Grid-Based Beetle Antennae Search Algorithm and Confusion-Aware Detection

Unmanned aerial vehicle target tracking is a complex task that encounters challenges in scenarios involving limited computing resources, real-time requirements, and target confusion. This research builds on previous work and addresses challenges by proposing a grid-based beetle antennae search algorithm and designing a lightweight multi-target detection and positioning method, which integrates interference-sensing mechanisms and depth information. First, the grid-based beetle antennae search algorithm’s rapid convergence advantage is combined with a secondary search and rollback mechanism, enhancing its search efficiency and ability to escape local extreme areas. Then, the You Only Look Once (version 8) model is employed for target detection, while corner detection, feature point extraction, and dictionary matching introduce a confusion-aware mechanism. This mechanism effectively distinguishes potentially confusing targets within the field of view, enhancing the system’s robustness. Finally, the depth-based localization of the target is performed. To verify the performance of the proposed approach, a series of experiments were conducted on the grid-based beetle antennae search algorithm. Comparisons with four mainstream intelligent search algorithms are provided, with the results showing that the grid-based beetle antennae search algorithm excels in the number of iterations to convergence, path length, and convergence speed. When the algorithm faces non-local extreme-value-area environments, the speed is increased by more than 89%. In comparison with previous work, the algorithm speed is increased by more than 233%. Performance tests on the confusion-aware mechanism by using a self-made interference dataset demonstrate the model’s high discriminative ability. The results also indicate that the model meets the real-time requirements.

Nonlinear FOPID controller design for pressure regulation of steam condenser via improved metaheuristic algorithm

Shell and tube heat exchangers are pivotal for efficient heat transfer in various industrial processes. Effective control of these structures is essential for optimizing energy usage and ensuring industrial system reliability. In this regard, this study focuses on adopting a fractional-order proportional-integral-derivative (FOPID) controller for efficient control of shell and tube heat exchanger. The novelty of this work lies in the utilization of an enhanced version of cooperation search algorithm (CSA) for FOPID controller tuning, offering a novel approach to optimization. The enhanced optimizer (en-CSA) integrates a control randomization operator, linear transfer function, and adaptive p-best mutation integrated with original CSA. Through rigorous testing on CEC2020 benchmark functions, en-CSA demonstrates robust performance, surpassing other optimization algorithms. Specifically, en-CSA achieves an average convergence rate improvement of 23% and an enhancement in solution accuracy by 17% compared to standard CSAs. Subsequently, en-CSA is applied to optimize the FOPID controller for steam condenser pressure regulation, a crucial aspect of heat exchanger operation. Nonlinear comparative analysis with contemporary optimization algorithms confirms en-CSA’s superiority, achieving up to 11% faster settling time and up to 55% reduced overshooting. Additionally, en-CSA improves the steady-state error by 8% and enhances the overall stability margin by 12%.

Microgrid energy management with renewable energy using gravitational search algorithm

Microgrid energy management with renewable energy using gravitational search algorithm

Flamingo Search Sailfish Optimizer Based SqueezeNet for Detection of Breast Cancer Using MRI Images

Breast cancer with increased risk in women is identified with Breast Magnetic Resonance Imaging (Breast MRI) and this helps in evaluating treatment therapies. Breast MRI is time time-consuming process that involves the assessment of current imaging. This research work depends on the detection of breast cancer at the earlier stages. Among various cancers, breast cancer in women occurs in larger accounts for almost 30% of estimated cancer cases. In this research, many steps are followed for breast cancer detection like pre-processing, segmentation, augmentation, extraction of features, and cancer detection. Here, the median filter is utilized for pre-processing, as well as segmentation is followed after pre-processing, which is done by Psi-Net. Moreover, the process of augmentation like shearing, translation, and cropping are followed after segmentation. Also, the segmented image tends to process feature extraction, where features like shape features, Completed Local Binary Pattern (CLBP), Pyramid Histogram of Oriented Gradients (PHOG), and statistical features are extracted. Finally, breast cancer is detected using the DL model, SqueezeNet. Here, the newly devised Flamingo Search SailFish Optimizer (FSSFO) is used in training Psi-Net as well as SqueezeNet. Furthermore, FSSFO is the combination of both the Flamingo Search Algorithm (FSA) and SailFish Optimizer (SFO).

SaDENAS: A self-adaptive differential evolution algorithm for neural architecture search

Evolutionary neural architecture search (ENAS) and differentiable architecture search (DARTS) are all prominent algorithms in neural architecture search, enabling the automated design of deep neural networks. To leverage the strengths of both methods, there exists a framework called continuous ENAS, which alternates between using gradient descent to optimize the supernet and employing evolutionary algorithms to optimize the architectural encodings. However, in continuous ENAS, there exists a premature convergence issue accompanied by the small model trap, which is a common issue in NAS. To address this issue, this paper proposes a self-adaptive differential evolution algorithm for neural architecture search (SaDENAS), which can reduce the interference caused by small models to other individuals during the optimization process, thereby avoiding premature convergence. Specifically, SaDENAS treats architectures within the search space as architectural encodings, leveraging vector differences between encodings as the basis for evolutionary operators. To achieve a trade-off between exploration and exploitation, we integrate both local and global search strategies with a mutation scaling factor to adaptively balance these two strategies. Empirical findings demonstrate that our proposed algorithm achieves better performance with superior convergence compared to other algorithms.

Meal Delivery Routing Problem with Stochastic Meal Preparation Times and Customer Locations

AbstractWe investigate the Meal Delivery Routing Problem (MDRP), managing courier assignments between restaurants and customers. Our proposed variant considers uncertainties in meal preparation times and future order numbers with their locations, mirroring real challenges meal delivery providers face. Employing a rolling-horizon framework integrating Sample Average Approximation (SAA) and the Adaptive Large Neighborhood Search (ALNS) algorithm, we analyze modified Grubhub MDRP instances. Considering route planning uncertainties, our approach identifies routes at least 25% more profitable than deterministic methods reliant on expected values. Our study underscores the pivotal role of efficient meal preparation time management, impacting order rejections, customer satisfaction, and operational efficiency.

Remaining Useful Life Prediction of Rolling Bearings Based on Adaptive Continuous Deep Belief Networks and Improved Kernel Extreme Learning Machine

ABSTRACTRolling bearings are crucial components in a wide variety of machinery. Monitoring their conditions and predicting their remaining useful life (RUL) is vital to prevent unexpected breakdowns, optimize maintenance schedules, and reduce operational costs. This article proposes an approach based on adaptive continuous deep belief networks (ACDBN) and improved kernel extreme learning machine (KELM) to predict the RUL of rolling bearings. In the proposed approach, the ACDBN model is used for extracting hidden fault features and the distance between the initial health state and the real‐time degradation state is used to construct a health indicator (HI). Then, a hybrid kernel extreme learning machine prediction model optimized by the sparrow search algorithm (SSA‐KELM) is proposed to estimate the RUL using the extracted HIs. The SSA is used to find the optimal parameters of the KELM model. The proposed method has been assessed using existing bearing datasets. The obtained results indicate that the proposed method successfully improves RUL prediction accuracy compared to existing approaches in the literature.

Parameter tuning for active disturbance rejection control of fixed-wing UAV based on improved bald eagle search algorithm

Purpose This paper aims to develop a method for tuning the parameters of the active disturbance rejection controller (ADRC) for fixed-wing unmanned aerial vehicles (UAVs). The bald eagle search (BES) algorithm has been improved, and a cost function has been designed to enhance the optimization efficiency of ADRC parameters. Design/methodology/approach A six-degree-of-freedom nonlinear model for a fixed-wing UAV has been developed, and its attitude controller has been formulated using the active disturbance rejection control method. The parameters of the disturbance rejection controller have been fine-tuned using the collaborative mutual promotion bald eagle search (CMP-BES) algorithm. The pitch and roll controllers for the UAV have been individually optimized to obtain the most effective controller parameters. Findings Inspired by the salp swarm algorithm (SSA), the interaction among individual eagles has been incorporated into the CMP-BES algorithm, thereby enhancing the algorithm's exploration capability. The efficient and accurate optimization ability of the proposed algorithm has been demonstrated through comparative experiments with genetic algorithm, particle swarm optimization, Harris hawks optimization HHO, BES and modified bald eagle search algorithms. The algorithm's capability to solve complex optimization problems has been further proven by testing on the CEC2017 test function suite. A transitional function for fitness calculation has been introduced to accelerate the ability of the algorithm to find the optimal parameters for the ADRC controller. The tuned ADRC controller has been compared with the classical proportional-integral-derivative (PID) controller, with gust disturbances introduced to the UAV body axis. The results have shown that the tuned ADRC controller has faster response times and stronger disturbance rejection capabilities than the PID controller. Practical implications The proposed CMP-BES algorithm, combined with a fitness function composed of transition functions, can be used to optimize the ADRC controller parameters for fixed-wing UAVs more quickly and effectively. The tuned ADRC controller has exhibited excellent robustness and disturbance rejection capabilities. Originality/value The CMP-BES algorithm and transitional function have been proposed for the parameter optimization of the active disturbance rejection controller for fixed-wing UAVs.

An Enhanced Approach for Optimal Design of Hybrid CCHP System Consisting Gas Turbine/Photovoltaic/Thermal Collectors

This paper proposes a hybrid technique for optimizing Combined Cooling, Heating, and Power (CCHP) systems integrating a diverse set of components including geothermal heat pumps, gas turbines, absorption chillers/heaters, and photovoltaic/thermal collectors, along with storage units like batteries and water tanks. The innovation lies in combining the Capuchin Search Algorithm (CapSA) with the Wild Horse Optimizer (WHO) to concurrently optimize primary energy savings, cost reductions, and carbon dioxide emissions. Parametric distributions are used in probabilistic strategies to resolve uncertainty in solar radiation and building load. CapSA optimizes the CCHP system’s design, while WHO enhances its performance. The effectiveness of the proposed strategy is demonstrated through implementation in MATLAB, where it achieves an average ACSR (Average Comprehensive Saving Rate) enhancement of 5.48%. This numerical result highlights a substantial improvement over existing methodologies, showcasing the superior performance of the hybrid optimization approach in enhancing energy efficiency and environmental sustainability of CCHP systems.

An adaptive gravitational search algorithm for optimizing mechanical engineering design and machining problems

The gravitational search algorithm (GSA) is widely used for solving optimization problems because it performs in a superior manner as compared to various competing evolutionary and swarm-based metaheuristics. However, GSA frequently gets trapped in local optima due to a lack of solution diversity. Although chaotic gravitational search algorithm (CGSA) can resolve this issue to some extent but its degraded exploitation rate and convergence speed may not result in desired outcome. To this end, diversity-based chaotic GSA (DCGSA) has exhibited its capability to resolve the issues encountered with GSA and CGSA to a certain extent for unconstrained problems. DCGSA achieves this characteristic through the use of an adaptive gravitational constant, which varies according to the diversity values of the population. Since most real-world problems are subjected to some constraints, it is prudent to improve the performance of GSA in solving constrained optimization problems. The present study integrates the enhanced search capability of DCGSA with a generalized constraint handling mechanism to improve the performance of GSA in solving constrained problems. It is observed that DCGSA significantly outperforms GSA on both CEC (Congress on evolutionary computation) 2006, 2010 and 2017 functions and competes strongly with CGSA. Diversity analysis shows that the capability to balance exploration and exploitation rates is enhanced using CGSA and DCGSA. Furthermore, DCGSA algorithms outperform GSA and CGSA on real-world machining and CEC 2020 mechanical design problems. Comparison with state-of-the-art algorithms is made to analyze the performance of the algorithms from a larger perspective.

Intrusion detection based on hybrid metaheuristic feature selection

Abstract The multidimensional features of network flows are the main data source for intrusion detection, but excessively low-value features generate accuracy and efficiency challenges. Researchers have used redundant feature reduction to simplify intrusion detections, and feature selection algorithms are beginning to be widely used. This paper presents a novel hybrid feature selection algorithm, CSA-FPA, which combines both a crow search algorithm and a flower pollination algorithm. In this method, properties such as local pollination and the levy flight of FPA are used to balance the global search and local search efficiencies, and parameters such as group distance and probability thresholds are introduced to customize the model’s appearance. The simulation results on the UNSW-NB15 and CIC-IDS2017 datasets show that the proposed CSA-FPA method achieves better detection accuracies than previous algorithms. Using the proposed feature selection method, the AdaBoost classifier achieved a detection accuracy of 99.14% on the CIC-IDS2017 dataset and 97.98% on the UNSW-NB15 dataset.

Compact widely tunable laser integrated on an indium phosphide membrane platform

We present the design, fabrication, and characterization results of a compact, widely tunable laser realized on an indium phosphide membrane-on-silicon (IMOS) platform. The laser features a compact Mach–Zehnder interferometric structure as the wavelength-selective intracavity filter with a footprint of 0.13 mm2. The filter design is optimized to ensure narrow filter transmission and high side-mode-to-main-mode-ratio, enabling single-mode operation for the laser. The high optical confinement on the IMOS platform can support tight waveguide bends. Leveraging this, the laser achieves a short cavity length, further enhancing the single-mode operation. Measurement results indicate a threshold current of 29 mA and a maximum on-chip output power of approximately 3.6 dBm and wall plug efficiency of 1.8%. The side-mode suppression ratio ranges from 30 to 44 dB, with a tuning range spanning 40 nm, from 1555 to 1595 nm. A complete tuning lookup table is generated via an automated setup incorporating a stochastic search algorithm.

A new accurate and fast convergence cuckoo search algorithm for solving constrained engineering optimization problems

In recent years, the Cuckoo Optimization Algorithm (COA) has been widely used to solve various optimization problems due to its simplicity, efficacy, and capability to avoid getting trapped in local optima. However, COA has some limitations such as low convergence when it comes to solving constrained optimization problems with many constraints. This study proposes a new modified and adapted version of the Cuckoo optimization algorithm, referred to as MCOA, that overcomes the challenge of solving constrained optimization problems. The proposed adapted version introduces a new coefficient that reduces the egg-laying radius, thereby enabling faster convergence to the optimal solution. Unlike previous methods, the new coefficient does not require any adjustment during the iterative process, as the radius automatically decreases along the iterations. To handle constraints, we employ the Penalty Method, which allows us to incorporate constraints into the optimization problem without altering its formulation. To evaluate the performance of the proposed MCOA, we conduct experiments on five well-known case studies. Experimental results demonstrate that MCOA outperforms COA and other state-of-the-art optimization algorithms in terms of both efficiency and robustness. Furthermore, MCOA can reliably find the global optimal solution for all the tested problems within a reasonable iteration number.

Design and development of different adaptive MPPT controllers for renewable energy systems: a comprehensive analysis

As of now, all over the world is focusing on the Electric Vehicle (EV) technology because its features are low environmental pollution, less maitainence cost required, high robustness, and good dynamic response. Also, the EVs work continuously until the input fuel is provided to the fuel stack. Here, a Proton Exchange Membrane Fuel Cell (PEMFC) is used as an input source to the electric vehicle system because of its merits fast startup, and quick response. However, the PEMFC gives nonlinear voltage versus current characteristics. As a result, the extraction of maximum power from the fuel stack is very difficult. The main aim of this work is to study different Maximum Power Point Tracking Techniques (MPPT) for the DC-DC converter-fed PEMFC system. The studied MPPT controllers are Adjusted Step Value of Perturb & Observe (ASV with P&O), Adaptive Step Size with Incremental Conductance (ASS with IC), Radial Basis Functional Network (RBFN), Incremental Step-Fuzzy Logic Controller (IS with FLC), Continuous Step Variation based Particle Swarm Optimization (CSV with PSO), and Adaptive Step Value-Cuckoo Search Algorithm (ASV with CSA). The selected MPPT controllers’ comprehensive study has been in terms of maximum power extraction, tracking speed of the MPP, settling time of the fuel stack output voltage, oscillations across the MPP, and design complexity. From the comprehensive performance results of the hybrid MPPT controllers, the ASV with CSA technique gives superior performance when equated to the other MPPT controllers.

Waste management and water quality evaluation prediction in urban environments through advanced robust hybrid machine learning algorithms

Water quality management is a crucial aspect of environmental protection, requiring the monitoring and regulation of effluent discharges into surface water bodies. This research introduces a novel approach to predicting Water Quality Evaluation (WQE) through a unique hybrid model, ABC-DWKNN-ICA, which integrates the Distance-weighted K-Nearest Neighbors (DWKNN) algorithm with the Artificial Bee Colony (ABC), Firefly Algorithm (FA), Imperialist Competitive Algorithm (ICA), and Gravitational Search Algorithm (GSA). Utilizing a comprehensive dataset of 1106 data points from Telangana, India, spanning 2018–2020, the study examines a range of water quality parameters, including Ground Water Level (GWL), Potential of Hydrogen (PH), Electrical Conductivity (EC), and others. The ABC-DWKNN-ICA model demonstrates exceptional performance in terms of Recall, Precision, Accuracy, and F1 Score for WQE prediction, distinguishing itself with enhanced feature selection, improved classification accuracy, robustness to noise and outliers, reduced dimensionality, and scalability to large datasets. This hybrid model represents a significant advancement over existing approaches, including traditional Hybrid Machine Learning (HML) algorithms such as ABC-DWKNN, FA-DWKNN, ICA-DWKNN, and GSA-DWKNN. By focusing on the capabilities of ABC-DWKNN-ICA rather than comparing all HML algorithms, the research highlights its superior effectiveness in water quality prediction, with performance metrics of 0.83 for Recall, 0.86 for Precision, 0.91 for Accuracy, and 0.86 for F1 Score. This study thus fills a critical research gap by demonstrating the model's value in environmental data analysis and offering promising prospects for more effective management of water resources. Additionally, feature selection, dimensionality reduction, enhanced accuracy, noise handling, and imbalanced dataset management are key advantages of the proposed model.

Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

AbstractWireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing methods, highlighting its potential for enhancing WMN performance. Specifically, CFL-PSO achieves up to a 66.5% improvement in network connectivity, a 16.56% improvement in coverage, and a 21.4% improvement in the objective function value when compared to the standard FLA.