Search Optimization Method Research Articles

Hybrid prediction models for assessing the Higher Education Institutions Performance in QS World Institution Rankings

Background Quality Education is one of the primary requirements for the best survival. Pursuing higher education in a highly reputed institutions makes much difference in shaping the career of the individual. As many ranking and accreditation boards for higher education institutions like NAAC is prevalent, World ranking distinguishes institution reputation globally. The QS World University Ranking is a vital gauge for learners, educators, and institutions all over the world, allowing them to analyze and compare the quality and reputation of higher education. Predicting these rankings is difficult due to data availability concerns and QS’s frequent methodology revisions. Subjectivity and narrow criteria in rankings hamper the assessment of university greatness even more. Machine learning, data scraping, model adaptability, algorithm reversal, and short-term predictions are some existing ways of dealing with these difficulties. In this research, a prediction model for assessing institution performance in the QS World institution Rankings is designed using hybrid machine learning algorithms and optimization techniques. Two algorithms surpass others in forecasting ranks, according to the analysis. These hybrid models improves prediction accuracy of QS world rankings by integrating data analysis with model optimization using particle swarm optimization and Tabu search method.

An improving secure communication using multipath malicious avoidance routing protocol for underwater sensor network

The Underwater Sensor Network (UWSN) comprises sensor nodes with sensing, data processing, and communication capabilities. Due to the limitation of underwater radio wave propagation, nodes rely on acoustic signals to communicate. The data gathered by these nodes is transmitted to coordinating nodes or ground stations for additional processing and analysis. The characteristics of UWSN with underwater channels make them vulnerable to malicious attacks. UWSN communication networks are particularly susceptible to malicious attacks owing to high bit error rates, significant propagation delay variations, and low bandwidth. Moreover, because of the challenging and erratic underwater conditions, limited bandwidth, slow data transmission speed, and power constraints of underwater sensor nodes establishing secure communication in UWSN presents a significant challenge. To address the issues mentioned above, we have introduced the Multipath Malicious Avoidance Routing Protocol (M2ARP) and Foldable Matrix based Padding Rail Fence Encryption Scheme (FM-PRFES) methods to enhance secure communication in UWSNs. The proposed FM-PRFES method encrypts the input data to prevent unauthorized access during transmission within the network. Subsequently, the proposed Energy Efficiency Node Selection (EENS) method is used to identify the significant nodes in the network. Additionally, the Cuckoo Search Optimization (CSO) method is utilized to select the Cluster Head (CH) for data transmission. Subsequently, M2ARP is employed to analyze various routes and avoid adversarial nodes in the network. As a result, the proposed experimental analysis yields more efficient results regarding security, Packet Delivery Ratio (PDR), and throughput performance than traditional approaches.

Comparative Study of Core Material's Stiffness on Sandwich Panel with Composite Face Sheets beyond the Yield Point

The study aims to explore the performance of sandwich panels exceeding the yield point stiffness of the core material. Sandwich panels have gained growing attention among designers owing to their excellent corrosion properties, and lightweight, and speedy installation process. They have been applied in numerous industrial sectors, including aerospace, architectural, marine, and transportation. Typically, sandwich panels are composed of a single central core sandwiched by a pair of outer face sheets, where the core is normally developed using softer materials compared to the face sheets. Given that past studies have primarily focused on sandwich panels in the elastic range, this present study explored the performance of sandwich panels exceeding the yield point stiffness of the core material. The univariate search optimization method was utilized to assess the elastic modulus ratio of the core (typically foam) to the face sheet (composite material). The load was elevated in a quasi-static order until the face sheets reached their yield point. Subsequently, the panel was simulated using the finite element analysis commercial package ANSYS APDL, with simply supported boundary conditions used on all sides of the panel. The proposed model was verified by comparing the numerical and experimental data from recent literature. Based on the results, the panel's increased load-carrying capacity corresponded as the core material stiffness exceeded its yield limit. Moreover, the transmission of load to the face sheets increased as the core stiffness decreased. In summary, stiffer core materials caused the sandwich panel to behave more as isotopic face sheets. Thus, the face sheets yielded ahead of the core material.

Research on a New Maximum Power Tracking Algorithm for Photovoltaic Power Generation Systems

INTRODUCTION: Significant advances have been made in photovoltaic (PV) systems, resulting in the development of new Maximum Power Point Tracking (MPPT) methods. The output of PV systems is heavily influenced by the varying performance of solar-facing PV panels under different weather conditions. Partial shading (PS) conditions pose additional challenges, leading to multiple peaks in the power-voltage (P-V) curve and reduced output power. Therefore, controlling MPPT under partial shading conditions is a complex task. OBJECTIVES: This study aims to introduce a novel MMPT algorithm based on the ant colony incorporated bald eagle search optimization (AC-BESO) method to enhance the efficiency of PV systems. METHODS: The effectiveness of the proposed MPPT algorithm was established through a series of experiments using MATLAB software, tested under various levels of solar irradiance. RESULTS: Compared to existing methods, the proposed AC-BESO algorithm stands out for its simplicity in implementation and reduced computational complexity. Furthermore, its tracking performance surpasses that of conventional methods, as validated through comparative analyses. CONCLUSION: This study confirms the efficacy of the AC-BESO method over traditional strategies. It serves as a framework for selecting an MPPT approach when designing PV systems.

Comparative analysis of direct search methods for material design optimization

This paper contributes to the field of optimization for periodic composite material design by systematically evaluating the performance of four direct search algorithms: greedy, simulated annealing (SA), genetic algorithms (GA), and particle swarm optimization (PSO). The central goal is to determine the optimal arrangement of two material domains inside a representative volume element (RVE) while respecting the constraint that half volume of the domains is composed of a specific material, with the effective shear modulus as the objective function. The main contribution of this work is the detailed comparison of these algorithms in terms of success rates and computational efficiency, providing critical insights into their suitability for different problem scales. For a small-scale problem (16 cells), PSO achieves a 100% success rate, while SA demonstrates over 90% success but at a higher computational cost. The greedy algorithm, despite its simplicity, achieves a 72% success rate with the lowest computational effort, requiring an average of 256 objective function evaluations. GA, however, exhibits the lowest success rate at 52% and requires the highest average number of evaluations (5003.2). Furthermore, the paper extends the analysis to a more complex problem (36 cells), where the adaptability and efficiency of the algorithms are tested. This investigation highlights the trade-offs between algorithmic complexity, success rate, and computational cost, offering practical guidelines for selecting suitable optimization methods based on problem size and characteristics.

Multi-objective optimization design method for the dimensions and control parameters of curling hexapod robot based on application performance

The curling hexapod robot is different from previous curling robots, which can mimic the throwing actions of curlers. To ensure its performance, the size and control parameters of the robot need to be optimized. However, there is still a lack of parameter design optimization methods for hexapod curling robots based on application performance and considering the coupling effects of size and control parameters. To meet this demand, this paper combines Simscape Multibody simulation modeling and genetic algorithm to propose a multi-objective optimization method for the dimensions and control parameters of curling hexapod robot based on application performance. This method uses Simscape Multibody to establish a parametric model of the curling robot, and proposes an optimization search method using genetic algorithms based on constraints and objective functions. Finally, the dimensions and control parameters of the curling robot that meet the application performance requirements are solved through the optimization method.

Aerodynamic Parameter Identification Based on Multi Group Adaptive Search Optimization Algorithm

Abstract Due to the strong coupling and nonlinearity of spinning-stabilized correction projectile’s motion and dynamic model, its aerodynamic parameter identification can be transformed into a high-dimensional multi extremum optimization problem. To avoid the excessive computational complexity and susceptibility to local optima, this paper proposes a multiple swarm adaptive search optimization method. This method automatically divides the samples into several subpopulations through an adaptive density peak clustering method, and then searches and updates in each subpopulation. To balance convergence speed and population diversity, the update operator for each generation combines the random search and optimal convergence step size, whose weights can be adaptively adjusted based on the update rate and dispersion characteristics of the subpopulations. Applying this method to the aerodynamic parameter identification, and comparing with improved particle swarm optimization and genetic algorithm, simulation results show that a better identification result can be obtained at the same iterations.

Evaluation of soil quality of cultivated lands with classification and regression-based machine learning algorithms optimization under humid environmental condition

In soil science, machine learning algorithms are preferred for pedotransfer functions due to their rapid data acquisition and high prediction accuracy. The current study aims to evaluate the prediction of soil quality in agricultural lands dominated by the humid Black Sea climate using various algorithms. Both classification and regression-based algorithms (Random Forest-RF, Light Gradient Boosting-LGB, Extreme Gradient Boosting-XGBoost, k-nearest neighbors-kNN, Logistic Regression, multilayer perceptron-MLP, Linear Regression-LR and Bayesian Ridge- BR) were used in the method. The comparison of soil maps is also included. Furthermore, the present study evaluates the Grid Search optimization method with K-Fold Cross Validation (K = 5) for both classification and regression-based algorithms. The prediction of soil quality was performed using class-based and regression-based algorithms. As a result of the study, the RF and XGBoost algorithms achieved an approximate accuracy rate of 92 % in the class-based prediction. In regression-based predictions, the most successful algorithms were BR and LR, with an R2 Score of 0.84. The Grid Search optimization method was used to improve the R2 Score, resulting in an increase to 0.90 and 0.88 for BR and LR, respectively. The optimized hyperparameters showed improved performance in predicting the soil quality index. The present study found that Gaussian and Spherical models had the lowest prediction errors in spatial distribution maps. Tree-based algorithms were found to be suitable for class-based prediction of soil quality, while the linear regression method was appropriate for regression predictions. This study is characterized by a rainy climate resulting in acidic soils with high organic matter content. Planning of new studies in different climates and soil properties is recommended.

Optimal allocation of EV charging station and capacitors considering reliability using a hybrid optimization approach

This paper proposes a novel approach for strategically placing EV charging stations (EVCS) and capacitors within electrical distribution networks (EDN) to mitigate issues arising from the widespread adoption of Electric Vehicles (EVs). The goal is to maximize the net present value (NPV) by reducing energy losses and minimizing system interruption costs while ensuring reliability. To achieve this, the paper introduces a hybrid optimization technique called HGP_CS, which combines Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Cuckoo Search optimization methods. By employing various compensation coefficients to assess failure rates and identify strategies for enhancing NPV, the approach aims to optimize the placement of EVCS and capacitors within the EDN. Validation is carried out in IEEE 33-bus and 118-bus systems to demonstrate the performance of the proposed approach. It is found that the proposed approach, compared to the base case, reduced the energy loss cost by 34.19% and 31.57% and also the Expected Interruption Costs (EIC) by 17.26% and 13.41%, for IEEE 33-bus and 118-bus system, respectively. Additionally, the proposed method is economically beneficial as it can attain higher profits. In IEEE 33- bus and 118 bus systems, the annual net profit using the proposed approach is 36,805 $/year and 271,670.72 $/year, respectively. Thus, the proposed technique enhances the overall system performance and significantly boosts profitability, making it a compelling choice for optimizing the placement of charging stations and reactive power sources in networks.

A robust joint frequency spectrum and power allocation strategy in a coexisting radar and communication system

The resource allocation technique is of great significance in achieving frequency spectrum coexistence in Joint Radar-Communication (JRC) systems, by which the problem of radio frequency spectrum congestion can be well alleviated. A Robust Joint Frequency Spectrum and Power Allocation (RJFSPA) strategy is proposed for the Coexisting Radar and Communication (CRC) system. Specifically, we consider the uncertainty of target Radar Cross Section (RCS) and communication channel gain to formulate a bi-objective optimization model. The joint probabilities that the Cramér-Rao Lower Bound (CRLB) of each target satisfying the localization accuracy threshold and the Communication Data Ratio (CDR) of each user satisfying the communication threshold are simultaneously maximized, under the constraint of the total power budget. A Three-Stage Alternating Optimization Method (TSAOM) is proposed to obtain the Best-Known Pareto Subset (BK-PS) of this problem, where the frequency spectrum, radar power, and communicator power are allocated using the greedy search and standard convex optimization methods, respectively. Simulation results confirm the effectiveness of the proposed RJFSPA strategy, compared with the resource allocation methods in a uniform manner and that ignores the uncertainties. The efficiency of the TSAOM is also verified by the comparison with the exhaustive search-based method.

Torque fault compensation in electric vehicle switched reluctance motor drives: A jellyfish search optimization method

AbstractIn this paper, an enhanced indirect instantaneous‐torque‐control is proposed based on the torque sharing function approach of switched reluctance motor drives for electric vehicles by employing the jelly fish search. The major goal is to attain vehicle desires that include minimal torque ripple, maximum torque per ampere (MTPA), and huge performance and extend speed limit. First, a simplest analytic design is developed a determine more proficient turn‐on angle for the torque product. Second, an altered torque sharing function (TSF) is used for compensating the faults of torque tracking. The proposed technique is calculated to represent an accurate switched reluctance motor and its magnetized features. They have worked to create the machine model and execute the necessary transmits. The torque fault is evaluated and compensated inside the torque sharing function. The adapting TSF is compensates for the torque fault with receiving the phase because it is the minimal flux rate connecting variation. Finally, the jellyfish search technique is accepted to determine the optimal control parameters. The proposed strategies are done in MATLAB and its performance is contrasted with different existing strategies. According to the simulation result, the proposed strategy‐based accuracy is 94.2% at 50 iteration and 80% at 100th iteration which is higher than the existing methods. From this analyses, it proved that the proposed technique gives superior performance to existing one.

Chemical reaction inspired approach for routing problems with hard time constraints

ABSTRACT One of the most common issues in logistics and transportation planning is the vehicle routing problem. The objective is to find minimum cost-vehicle routes for serving a set of dispersed customers with deterministic demands while satisfying some constraints. In this paper, we focus on two main constraints, which are vehicle capacity and time windows for customers. We propose to tackle this NP-hard problem with a bio-inspired approach based on a hybridization of Chemical Reaction Optimization and local search method. The solving process of our approach mimics the chemical reactions between molecules to attain their stability with lowest energy. For more diversification of the search space, we involved in the initialization stage a local search method based on greedy adaptive procedure. The performances of our hybrid approach are assessed on the base of various benchmark instances. Simulation results show the good performances and the efficiency of the proposed approach.

Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve.

Accurate segmentation of retinal layers in optical coherence tomography (OCT) images is critical for assessing diseases that affect the optic nerve, but existing automated algorithms often fail when pathology causes irregular layer topology, such as extreme thinning of the ganglion cell-inner plexiform layer (GCIPL). Deep LOGISMOS, a hybrid approach that combines the strengths of deep learning and 3D graph search to overcome their limitations, was developed to improve the accuracy, robustness and generalizability of retinal layer segmentation. The method was trained on 124 OCT volumes from both eyes of 31 non-arteritic anterior ischemic optic neuropathy (NAION) patients and tested on three cross-sectional datasets with available reference tracings: Test-NAION (40 volumes from both eyes of 20 NAION subjects), Test-G (29 volumes from 29 glaucoma subjects/eyes), and Test-JHU (35 volumes from 21 multiple sclerosis and 14 control subjects/eyes) and one longitudinal dataset without reference tracings: Test-G-L (155 volumes from 15 glaucoma patients/eyes). In the three test datasets with reference tracings (Test-NAION, Test-G, and Test-JHU), Deep LOGISMOS achieved very high Dice similarity coefficients (%) on GCIPL: 89.97±3.59, 90.63±2.56, and 94.06±1.76, respectively. In the same context, Deep LOGISMOS outperformed the Iowa reference algorithms by improving the Dice score by 17.5, 5.4, and 7.5, and also surpassed the deep learning framework nnU-Net with improvements of 4.4, 3.7, and 1.0. For the 15 severe glaucoma eyes with marked GCIPL thinning (Test-G-L), it demonstrated reliable regional GCIPL thickness measurement over five years. The proposed Deep LOGISMOS approach has potential to enhance precise quantification of retinal structures, aiding diagnosis and treatment management of optic nerve diseases.

Adaptive Resource Scheduling Algorithm for Multi-Target ISAR Imaging in Radar Systems

Inverse synthetic-aperture radar (ISAR) can achieve precise imaging of targets, which enables precise perception of battlefield information, and it has become one of the most important tasks for radar systems. In multi-target scenarios, a resource scheduling method is required to improve the sensing ability and the overall efficiency of a radar system due to the limited resources. Considering the motion state of the target will change as the observation distance increases and image defocusing can occur due to the prolonged coherence accumulation time and significant changes in the target’s motion state, the optimal observation period should be an important consideration factor in the resource scheduling method to further improve the imaging efficiency of radar system, which has not yet been involved in existing research. In this paper, we first derive the expressions of the target’s effective rotation angle and the equivalent rotation angular velocity and then define the target’s optimal observation period. Then, for multi-target imaging scenarios, we allocate pulse resources within a given time period based on sparse-aperture ISAR imaging technology. An adaptive radar resource scheduling algorithm for multi-target ISAR imaging is proposed, which prioritizes allocating resources based on the optimal observation periods for the targets. In the algorithm, a radar resource scheduling model for multi-target ISAR imaging is established, and a feedback-based closed-loop search optimization method is proposed to solve the model. Finally, the best scheduling strategy can be obtained, which includes imaging task duration and the pulse allocation sequence for each target. Simulation results validate the effectiveness of the algorithm.

Study of the Geometry of an Oscillating Water Column Device with Five Chambers Coupled under Regular Waves through the Constructal Design Method

This research conducts a numerical study of a wave energy converter (WEC) device with five coupled hydropneumatic chambers, operating based on the principle of an oscillating water column (OWC). A turbine was not included, only considering the tube without it. The computational domain was defined by a wave channel housing an OWC device subjected to regular incident waves. The central objective was to assess the impact of chamber geometry on maximizing the total hydropneumatic power in energy conversion. The numerical simulations consider the pressure, mass flow rate, and total hydropneumatic power, with the latter being the performance indicator. To determine the geometries to be analyzed, the Constructal Design method was employed in conjunction with the exhaustive search optimization method to maximize the performance indicator. The degrees of freedom defined were the ratios between the height (Hn) and the length (Ln) of the hydropneumatic chambers (Hn/Ln, where n varies from one to five). Based on the results of the mass flow rate and pressure, their influence on power was evaluated. It was observed that the influence of the degrees of freedom on the pressure difference, mass flow rate, and hydrodynamic power was quite similar, displaying an increase for low ratios of Hn/Ln up to a maximum magnitude and followed by a decrease in magnitude. The best performance was achieved for the geometric configuration with Hn/Ln = 0.2613 (Hn = 5.0625 m and Ln = 15.8219 m), representing an improvement of 98.6% compared to the worst case analyzed.

Lattice layout and optimizer effect analysis for generating optimal transcranial electrical stimulation (tES) montages through the metaheuristic L1L1 method.

This study focuses on broadening the applicability of the metaheuristic L1-norm fitted and penalized (L1L1) optimization method in finding a current pattern for multichannel transcranial electrical stimulation (tES). The metaheuristic L1L1 optimization framework defines the tES montage via linear programming by maximizing or minimizing an objective function with respect to a pair of hyperparameters. In this study, we explore the computational performance and reliability of different optimization packages, algorithms, and search methods in combination with the L1L1 method. The solvers from Matlab R2020b, MOSEK 9.0, Gurobi Optimizer, CVX's SeDuMi 1.3.5, and SDPT3 4.0 were employed to produce feasible results through different linear programming techniques, including Interior-Point (IP), Primal-Simplex (PS), and Dual-Simplex (DS) methods. To solve the metaheuristic optimization task of L1L1, we implement an exhaustive and recursive search along with a well-known heuristic direct search as a reference algorithm. Based on our results, and the given optimization task, Gurobi's IP was, overall, the preferable choice among Interior-Point while MOSEK's PS and DS packages were in the case of Simplex methods. These methods provided substantial computational time efficiency for solving the L1L1 method regardless of the applied search method. While the best-performing solvers show that the L1L1 method is suitable for maximizing either focality and intensity, a few of these solvers could not find a bipolar configuration. Part of the discrepancies between these methods can be explained by a different sensitivity with respect to parameter variation or the resolution of the lattice provided.

Implementation of FOPID controller with modified harmony search optimization for precise modelling and auto-tuning of nonlinear systems

This research aims to optimize the fractional order proportional integral derivative (FOPID) controller for nonlinear process tests. Instead of relying on the traditional trial-and error method for dynamic parameter selection, this proposes a modified harmony search optimization method to compute the optimal dynamic parameters. Based on the physical parameters, using mathematical modelling techniques system transfer function is determined. Additionally, the delay of the control scheme is determined using the Open Loop Transfer Function (OLTF) response. The dynamic parameters of the FOPID controller are evaluated using the Harmonic Search Algorithm – Fractional Order Proportional Integral Derivative (HAS_FOPID) optimization technique, which aims to minimize the system's Integral Square Error (ISE). Time responses, including rise time, peak time, and peak overshoot, are obtained and optimized through the HAS_FOPID algorithm. To verify systems stability, the output response is analysed using various techniques such as bode plots, pole placement, and Nyquist plots. Furthermore, the HAS_FOPID optimization technique is compared with other natural optimization techniques to assess its effectiveness. The research also evaluates the system's robustness by supply/ load disturbances. The objective is to demonstrate that, the optimized HAS_FOPID technique, has significantly enhanced control performance, stability, and robustness of nonlinear systems compared to natural and alternative optimization approaches.

A novel extended rule-based system based on K-Nearest Neighbor graph

The Belief Rule-Based (BRB) system faces the rule combination explosion issue, making it challenging to construct the rule base efficiently. The Extended Belief Rule-Based (EBRB) system offers a solution to this problem by using data-driven methods. However, using EBRB system requires the traversal of the entire rule base, which can be time-consuming and result in the activation of many irrelevant rules, leading to an incorrect decision. Existing search optimization methods can somewhat solve this issue, but they have limitations. Moreover, the calculation of the rule activation weight only considers the similarity between input data and a single rule, ignoring the influence of the rule linkage. To address these problems, we propose a new EBRB system based on the K-Nearest Neighbor graph index (Graph-EBRB). We introduce the Hierarchical Navigable Small World (HNSW) algorithm to create the K-Nearest Neighbor graph index of the EBRB system. This index allows us to efficiently search and activate a set of key rules. We also propose a new activation weight calculation method based on the Graph Convolution Neural Network (GCN), and we optimize the system performance using a parameter learning strategy. We conduct a comprehensive experiment on 14 commonly used public data sets, and the results show that Graph-EBRB system significantly improves the reasoning efficiency and accuracy of the EBRB system. Finally, we apply the Graph-EBRB system to tree disease identification and achieve excellent classification performance, identifying over 90% of the diseased trees on the complete dataset.

Optimization method of the cam curve for acontinuous zoom optical system.

To make the driving force of a zoom cam cylinder stable and easy to control, the correlation between the cam curves of the zoom group and the compensation group is established effectively by reasonably selecting and arranging the coordinate system.Two optimization methods of cam curve are proposed. First, the original data are processed directly by the function of the target cam curve, and then the dynamic parameter such as the pressure angle is verified again to obtain the expected cam curve. This optimization method is simple and convenient, and a variety of optimization results can be constructed. Second, a physical model of multiple cam curves and the driving force with only one variable is constructed. The step search optimization method is used to process the original data point by point, and the best cam curve matching the model is obtained. Through dynamic simulation, it is determined that the cam curves designed by this optimization method can drive the zoom group and compensation group to complete the whole continuous zoom movement with a stable torque.

Optimization Design of Oil Groove Shape on the Surface of Friction Plate in High-Speed Wet Clutch Aimed at Minimizing Drag Torque

Abstract The wet clutch, a crucial component of the vehicle transmission system, experiences high-frequency impact and friction between the friction plate and the steel plate under high-speed separation conditions. This leads to a significant increase in drag torque, which is detrimental to the transmission efficiency and reliability of the high-speed wet clutch. This article aims to reduce the high-speed drag torque in a no-load wet clutch by optimizing the oil groove shape on the surface of the friction plate. Initially, a theoretical model of high-speed drag torque for the friction plate with arbitrary oil groove shape is established. Subsequently, a low-drag oil groove shape optimization model is proposed by establishing design parameters, constraint conditions, and an objective function, combined with an experiment design method, approximation modeling, and a global search optimization method. Finally, the stiffness and damping coefficients of the clearance flow field and the drag torque with the optimized oil groove shape are calculated. The simulation results and the proposed optimization method are validated through experiments. The experiment indicates that a friction plate with the optimized oil groove shape can significantly decrease high-speed drag torque, demonstrating strong practical engineering value.