Search Optimization Research Articles

Automated parking trajectory planning: a hybrid curve search and nonlinear optimisation approach

ABSTRACT This paper focuses on the problem of automatic parking trajectory planning in unstructured scenarios. The proposed algorithm adopts a combination of graph search and optimization strategy. First, A* search is utilized to obtain paths connecting the global start and end points. Second, the path is divided into three segments and subpaths are searched using the hybrid A* algorithm and the proposed A*-iBspline algorithm. Finally, the three subpaths are merged and a valid initial guess is generated for the OCP. Additionally, an iterative framework is used to improve the quality of the OCP solution. Simulation results show that the proposed algorithm has a significant advantage over existing methods in terms of time and applicability.

Emission Rate Estimation of Industrial Air Pollutant Emissions Based on Mobile Observation

Mobile observation has been widely used in the monitoring of air pollution. However, studies on pollution sources and emission characteristics based on mobile navigational observation are rarely reported in the literature. A method for quantitative source analysis for industrial air pollutant emissions based on mobile observations is introduced in this paper. NOx pollution identified in mobile observations is used as an example of the development of the method. A dispersion modeling scheme that fine-tuned the meteorological parameters according to the actual meteorological conditions was adopted to minimize the impact of uncertainties in meteorological conditions on the accuracy of small-scale dispersion modeling. The matching degree between simulated and observed concentrations was effectively improved through this optimization search. In response to the efficiency requirements of source resolution for multiple sources, a random search algorithm was first used to generate candidate solution samples, and then the solution samples were evaluated and optimized. Meanwhile, the new index Smatch was established to evaluate the quality of candidate samples, considering both numerical error and spatial distribution error of concentration, in order to address the non-uniqueness of the solution in the multi-source problem. Then, the necessity of considering the spatial distribution error of concentration is analyzed with the case study. The average values of NOx emission rates for the two study cases were calculated as 69.8 g/s and 70.8 g/s. The Smatch scores were 0.92–0.97 and 0.92–0.99. The results were close to the online monitoring data, and this kind of pollutant emission monitoring based on the mobile observation experiment was initially considered feasible. Additional analysis and clarifications were provided in the discussion section on the impact of uncertainties in meteorological conditions, the establishment of a priori emission inventories, and the interpretation of inverse calculation results.

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.

Optimization of submarine submersible search and rescue path based on simulated annealing

Optimization of submarine submersible search and rescue path based on simulated annealing

Improved Osprey Optimization Algorithm Based on Two-Color Complementary Mechanism for Global Optimization and Engineering Problems.

Aiming at the problem that the Osprey Optimization Algorithm (OOA) does not have high optimization accuracy and is prone to falling into local optimum, an Improved Osprey Optimization Algorithm Based on a Two-Color Complementary Mechanism for Global Optimization (IOOA) is proposed. The core of the IOOA algorithm lies in its unique two-color complementary mechanism, which significantly improves the algorithm's global search capability and optimization performance. Firstly, in the initialization stage, the population is created by combining logistic chaos mapping and the good point set method, and the population is divided into four different color groups by drawing on the four-color theory to enhance the population diversity. Secondly, a two-color complementary mechanism is introduced, where the blue population maintains the OOA core exploration strategy to ensure the stability and efficiency of the algorithm; the red population incorporates the Harris Hawk heuristic strategy in the development phase to strengthen the ability of local minima avoidance; the green group adopts the strolling and wandering strategy in the searching phase to add stochasticity and maintain the diversity; and the orange population implements the optimized spiral search and firefly perturbation strategies to deepen the exploration and effectively perturb the local optimums, respectively, to improve the overall population diversity, effectively perturbing the local optimum to improve the performance of the algorithm and the exploration ability of the solution space as a whole. Finally, to validate the performance of IOOA, classical benchmark functions and CEC2020 and CEC2022 test sets are selected for simulation, and ANOVA is used, as well as Wilcoxon and Friedman tests. The results show that IOOA significantly improves convergence accuracy and speed and demonstrates high practical value and advantages in engineering optimization applications.

Prediction model of moisture content in spray drying of ceramic slurry based on IMFO-BPNN

The final particle moisture content of ceramic slurry spray drying affects the processing, product quality, and process energy consumption. While the real-time moisture content determination is very difficult for spray drying process due to nonlinear, severe lag, interference factors, and complexity of the systems, the high temperature, high humidity drying environment and short drying process. In current work, an improved moth optimization (IMFO) algorithm combined with backpropagation neural network (BPNN) was proposed to predict the moisture content of ceramic slurry particles during spray drying. The performance of the model is trained and tested. Results demonstrate that IMFO has better convergence ability and speed compared to other algorithms such as particle swarm optimization (PSO) and gravitational search algorithm (GSA). The IMFO-BPNN model achieves a MAE of 0.0293, RMSE of 0.0383, and R2 value of 0.9113, outperforming the prediction performance of BPNN and MFO-BPNN models. The absolute error rate of the IMFO-BPNN model (0–5%) is lower compared to BPNN (5–10%) and MFO-BPNN model (>10%), showcasing superior accuracy in predicting moisture content. This mathematical model established in the study provides an efficient, accurate, and nondestructive method for predicting the drying endpoint of ceramic slurry spray drying.

Energy-exergy-economic-environmental (4E) analysis and multi-objective optimization of a cascade refrigeration system

The present work focusses on 4E analysis of a 50 kW cooling capacity cascade refrigeration cycle covering the aspects of energy, exergy, economic and environment analysis. The numerical investigation and the multi-objective optimization is carried out for the system using the refrigerant pair R170-R600a and R41-R600a. The refrigerant pair is selected based on the environmental implications in terms of GWP and ODP. Multi-objective optimization of the objective functions is carried out using a heat transfer search optimization algorithm to evaluate the optimal performance of the system. The effect of evaporator temperature, condenser temperature, LTC condenser temperature and LTC condenser temperature difference on the exergy efficiency and total cost of the system is studied. A set of multiple optimal solutions is presented using the Pareto optimal curve and TOPSIS criteria is employed to select the optimal operating condition. Compared to the refrigerant pair R170-R600a, the system with R41-R600a operates at better exergy efficiency and lower total cost. At the TOPSIS selected optimal condition, exergy efficiency and the total cost of the CRS is 63.5 % and 65,228 $/year for the refrigerant pair R41-R600a and 62.6 % and 67,690 $/year for R170-R600a, respectively. The distribution of variables shows that the effect of the evaporation temperature, condensation temperature and the LTC condenser temperature is profound in obtaining the optimal solution.

Research on heliostat field design based on mesh search optimization algorithm

Research on heliostat field design based on mesh search optimization algorithm

Retraction Note: An effective compensation of power quality issues using MPPT-based cuckoo search optimization approach

Retraction Note: An effective compensation of power quality issues using MPPT-based cuckoo search optimization approach

Hybridizing flower pollination algorithm with particle swarm optimization for enhancing the performance of IPv6 intrusion detection system

Internet Protocol version 6 (IPv6) is the most recent iteration of IP, designed to accommodate hundreds of thousands of devices with unique IP addresses. IPv6 introduces new features, such as the Neighbor Discovery Protocol (NDP) and Address Auto-configuration Scheme. For its effective operation, IPv6 relies on several protocols, including ICMPv6, which carries significant responsibilities. Similar to IPv4, IPv6 is susceptible to various attacks, including newer types like DDoS attacks executed via ICMPv6 messages, posing serious security and financial threats. Consequently, an Intrusion Detection System (IDS) is essential to safeguard against these attacks. IDS continuously evolve to incorporate features that can accurately detect such threats. However, feature selection strategies, particularly bio-inspired algorithms, often yield incorrect subsets of features. During machine learning processes, these inaccuracies impede the detection accuracy of DDoS attacks using ICMPv6. Many Optimization Search Algorithms become trapped in local minima and fail to consider multi-objective approaches, resulting in suboptimal feature selection. To address this, optimizing a bio-inspired algorithm within an IPv6 network has been proposed. Specifically, hybridizing the MFPA algorithm with the PSO algorithm is suggested to enhance detection accuracy. The selected features are used to train the dataset with a Support Vector Machine (SVM) classifier. The proposed approach is evaluated using the ICMPv6 dataset on various attacks, demonstrating superior classification accuracy of 97.99 %. It also reduced the number of features from 19 to 8, showcasing its efficiency.

A comprehensive analysis of the optimal GWO based FOPID MPPT controller for grid-tied photovoltaics system under atmospheric uncertainty

Recent years have seen an increase in the deployment of renewable energy sources on power system networks as a result of the gradual development of renewable energy sources. In order to utilize these intermittent sources, grid integration is crucial, since weather conditions are hard to predict accurately and do not match the pattern of generation. As a result, controlling energy becomes an unavoidable challenge when the alternative energy source is connected to the grid in a distributed manner. A number of factors contribute to the problem, including sporadic sources, daytime expenses, and limitations concerning the specifications of solar panels. Therefore, a more robust control system is needed to enhance the reliability and efficiency of the renewable energy integrated power network. Thus, this manuscript presents the design, implementation, and performance of an improved fractional order PID (FOPID) controller for proposed grid-connected photovoltaic systems. To maximize the power from the photovoltaic source, the controller is designed to extract as much energy as possible. In addition to having the adaptive nature of a PID controller, the proposed FOPID controller is capable of optimizing its gain parameter according to the generator and grid side parameters being considered. The proposed study utilized grey wolf optimization (GWO) to tune the FOPID controller for tracking the quadrature axis model, DC link voltage and current regulation, and maximizing the maximum power point. Further, FOPID is used to perform the system's current control functions, and each time the error is measured, the regulating parameters are updated accordingly. This paper contrasts fuzzy logic controllers (FLC), flying squirrel search optimization (FSSO), and PSO-tuned FOPID controllers with the proposed work. The simulation's results are displayed and examined in the section on results and outcomes. The obtained results demonstrate maximum solar power output under erratic weather conditions, validating the effectiveness of the proposed controller.

SPIN-Based Linear Temporal Logic Path Planning for Ground Vehicle Missions with Motion Constraints on Digital Elevation Models.

Linear temporal logic (LTL) formalism can ensure the correctness of mobile robot planning through concise, readable, and verifiable mission specifications. For uneven terrain, planning must consider motion constraints related to asymmetric slope traversability and maneuverability. However, even though model checker tools like the open-source Simple Promela Interpreter (SPIN) include search optimization techniques to address the state explosion problem, defining a global LTL property that encompasses both mission specifications and motion constraints on digital elevation models (DEMs) can lead to complex models and high computation times. In this article, we propose a system model that incorporates a set of uncrewed ground vehicle (UGV) motion constraints, allowing these constraints to be omitted from LTL model checking. This model is used in the LTL synthesizer for path planning, where an LTL property describes only the mission specification. Furthermore, we present a specific parameterization for path planning synthesis using a SPIN. We also offer two SPIN-efficient general LTL formulas for representative UGV missions to reach a DEM partition set, with a specified or unspecified order, respectively. Validation experiments performed on synthetic and real-world DEMs demonstrate the feasibility of the framework for complex mission specifications on DEMs, achieving a significant reduction in computation cost compared to a baseline approach that includes a global LTL property, even when applying appropriate search optimization techniques on both path planners.

New limit on dark photon kinetic mixing in the 0.2–1.2 μeV mass range from the Dark E-field Radio experiment

We report new limits on the kinetic mixing strength of the dark photon spanning the mass range 0.21−1.24 μeV corresponding to a frequency span of 50–300 MHz. The Dark E-field Radio experiment is a wideband search for dark photon dark matter. In this paper we detail changes in calibration and upgrades since our proof-of-concept pilot run. Our detector employs a wide-bandwidth E-field antenna moved to multiple positions in a shielded room, a low noise amplifier, wideband analog-to-digital converter, followed by a 224-point fast Fourier transform. An optimal filter searches for signals with Q≈106. In nine days of integration, this system is capable of detecting dark photon signals corresponding to a kinetic mixing strength ε several orders of magnitude lower than previous limits. We find a 95% exclusion limit on ε over this mass range between 6×10−15 and 6×10−13, tracking the complex resonant mode structure in the shielded room. Published by the American Physical Society 2024

RTMFusion: An enhanced dual-stream architecture algorithm fusing RGB and depth features for instance segmentation of tomato organs

Accurate and rapid acquisition of semantically annotated point clouds from tomato cultivation regions is crucial for autonomous tomato harvesting robots. This entails the robot’s ability to perceive tomato point clouds from various locations and identify the stems and pedicels connecting the fruits. However, amidst background interference from similar stem and pedicel features, the heterogeneous growth postures of tomato clusters and the slender and short nature of pedicels pose challenges in semantic point cloud segmentation and matching of different tomato fruits, pedicels, and stems. Leveraging the salient feature representation of depth images in fine contours and distance dimensions, this paper introduces RTMFusion, a lightweight dual-stream backbone instance segmentation model that integrates color images and depth images. A Depth Enhanced Feature Fusion module is designed for multi-modal feature fusion. Fruit-bearing organ matching is facilitated by a method based on distance constraints and conditional search. Key characteristics are extracted from semantic point clouds, and an optimal condition search is conducted on candidates meeting constraint conditions. On the test dataset, our model improved the mask mAP by 4.6 % compared to the baseline, achieving 72.1 % with a speed of 17.1 ms. Our matching method achieved 99.2 % pedicel matching accuracy and 97.3 % stem matching accuracy on 150 greenhouse samples. These results demonstrate the effectiveness of accurately identifying and matching tomato organs in real-world scenarios, addressing the challenge from complex greenhouse environments. This integrated visual system enhances the success rate of visual identification and the precision of cutting position localization for tomato harvesting robots.

Optimized brain tumor identification via graph sample and aggregate-attention network with Artificial Lizard Search Algorithm

A brain tumour is an abnormal growth of brain nerves that interferes with normal brain function.It causes a great deal of deaths. Timely detection and treatment are essential for saving the lives from this illness. Locating tumor-affected brain cells is a tedious, time-consuming process. In this manuscript, a Graph Sample and Aggregate-Attention Network with Artificial Lizard Search Optimization Algorithm for identification of brain tumour (GSAAN-ALSOA-BTI) is proposed. Initially, the input imageries are obtained from Figshare Brain Tumor Dataset. The images are preprocessed using Data-Adaptive Gaussian Average Filtering (DAGAF) to eliminate the unnecessary noise from image frames. The pre-processed image is given into the Multi kernel k-Means clustering (MKKMC) for image segmentation to identify boundaries in an image. Finally, the extracted feature features are given to Graph Sample with Aggregate-Attention Network (GSAAN) for categorizing the brain tumour identification as benign and malignant tumors. In general, GSAAN does not express any adaption of optimization techniques for determining the ideal parameters to assure exact classification of brain tumour identification. Thus, the Artificial Lizard Search Optimization Algorithm (ALSOA) is proposed to improve the weight parameter of GSAAN, which accurately categorizes the brain tumour identification of brain tumor. The proposed model is executed in Python and its efficacy is evaluated utilizing performance metrics like accuracy, precision, recall, FI-score, error rate, computation time and ROC. The GSAAN-ALSOA-BTI method provides higher accuracy, higher precision, higher recall compared with existing methods.

Construction of effective reproduction number of infectious disease individuals based on spatiotemporal discriminant search model: take hand-foot-mouth disease as an example

ObjectiveIn order to facilitate the tracing of infectious diseases in a small area and to effectively carry out disease control and epidemiological investigations, this research proposes a novel spatiotemporal model to estimate effective reproduction number(Re)for infectious diseases, based on the fundamental concept of contact tracing.MethodsThis study utilizes the incidence of hand, foot, and mouth disease (HFMD) among children in Bishan District, Chongqing, China from 2015 to 2019. The study incorporates the epidemiological characteristics of HFMD and aims to construct a Spatiotemporal Correlation Discrimination of HFMD. Utilizing ARC ENGINE and C# programming for the creation of a spatio-temporal database dedicated to HFMD to facilitate data collection and analysis. The scientific validity of the proposed method was verified by comparing the effective reproduction number obtained by the traditional SEIR model.ResultsWe have ascertained the optimal search radius for the spatiotemporal search model to be 1.5 km. Upon analyzing the resulting Re values, which range from 1.14 to 4.75, we observe a skewed distribution pattern from 2015 to 2019. The median and quartile Re value recorded is 2.42 (1.98, 2.72). Except for 2018, the similarity coefficient r of the years 2015, 2016, 2017, and 2019 were all close to 1, and p <0.05 in the comparison of the two models, indicating that the Re values obtained by using the search model and the traditional SEIR model are correlated and closely related. The results exhibited similarity between the Re curves of both models and the epidemiological characteristics of HFMD. Finally, we illustrated the regional distribution of Re values obtained by the search model at various time intervals on Geographic Information System (GIS) maps which highlighted variations in the incidence of diseases across different communities, neighborhoods, and even smaller areas.ConclusionThe model comprehensively considers both temporal variation and spatial heterogeneity in disease transmission and accounts for each individual's distinct time of onset and spatial location. This proposed method differs significantly from existing mathematical models used for estimating Re in that it is founded on reasonable scientific assumptions and computer algorithms programming that take into account real-world spatiotemporal factors. It is particularly well-suited for estimating the Re of infectious diseases in relatively stable mobile populations within small geographical areas.

Prediction of Optimal Design Parameters for Reinforced Soil Embankments with Wrapped Faces Using a GA–BP Neural Network

Under the same geological conditions, the thickness and length of the reinforced strip, the slope ratio of the reinforced embankment, the modulus of elasticity of the fill and the reinforced strip, and the friction angle at the interface between the reinforcement and the soil, are the main design parameters that have an important influence on the stress, deformation, and stability of the encompassing reinforced soil embankment. To quickly and accurately determine the optimal design parameters for reinforced soil embankments with wrapped faces, ensuring minimal cost, while maintaining structural safety, we propose a design parameter prediction model based on a GA–BP neural network. This model evaluates parameters within their specified ranges, using maximum lateral displacement, maximum vertical displacement, maximum stress in the XZ direction, the maximum shear strain increment, and the safety factor, as assessment criteria. The primary objective is to minimize the overall cost of the embankment. A comparison with five machine learning algorithms shows that the model has high prediction accuracy, and the optimal design parameter combinations obtained from the optimization search can significantly reduce the cost of the embankment, while controlling the displacement and stability of the embankment. Therefore, the GA–BP network is suitable for predicting the optimal design parameters of reinforced soil embankments with wrapped faces.

Time series prediction model using LSTM-Transformer neural network for mine water inflow

Mine flooding accidents have occurred frequently in recent years, and the predicting of mine water inflow is one of the most crucial flood warning indicators. Further, the mine water inflow is characterized by non-linearity and instability, making it difficult to predict. Accordingly, we propose a time series prediction model based on the fusion of the Transformer algorithm, which relies on self-attention, and the LSTM algorithm, which captures long-term dependencies. In this paper, Baotailong mine water inflow in Heilongjiang Province is used as sample data, and the sample data is divided into different ratios of the training set and test set in order to obtain optimal prediction results. In this study, we demonstrate that the LSTM-Transformer model exhibits the highest training accuracy when the ratio is 7:3. To improve the efficiency of search, the combination of random search and Bayesian optimization is used to determine the network model parameters and regularization parameters. Finally, in order to verify the accuracy of the LSTM-Transformer model, the LSTM-Transformer model is compared with LSTM, CNN, Transformer and CNN–LSTM models. The results prove that LSTM-Transformer has the highest prediction accuracy, and all the indicators of its model are well improved.

Exploring The Efficiency of Metaheuristics in Optimal Hyperparameter Tuning for Ensemble Models on Varied Data Modalities

Effective disease detection systems play an important role in healthcare by supporting diagnosis and treatment. This study provides a comparison of hyperparameter tuning methods for disease detection systems using four health datasets; kidney disease, diabetes detection, heart disease and breast cancer detection. The main objective of this research is to prepare datasets by normalizing the input and testing machine learning models such as Naive Bayes Support Vector Machine (SVM), Logistic Regression and k Nearest Neighbor (kNN). to identify effective models for each data set. After implementing the models, we apply three hyperparameter tuning techniques: Grid search, random search, and particle ensemble optimization (PSO). These methods are used to tune the model parameters. Improve overall performance metrics. The evaluation focuses on accuracy measurements to compare model performance before and after hyperparameter tuning. The results of this study illustrate how different tuning techniques can improve the performance of disease detection systems across a range of healthcare datasets. By conducting testing and analysis, we determine the appropriate tuning method for each data set, yielding valuable insights, to develop an accurate and effective disease detection system .These discoveries serve to advance the field of healthcare analytics and machine learning to deliver outcomes for patients and healthcare services.

Forecasting China's renewable energy consumption using a novel dynamic fractional-order discrete grey multi-power model

Accurately predicting renewable energy consumption is crucial for sustainable social and economic development, especially in China during its energy transition. This research introduces a novel dynamic fractional-order discrete grey multi-power model (DFDGMM(1,1,N)) to enable accurate forecasting of renewable energy consumption in China. The proposed method introduces a fractional-order accumulation operator and three power exponents that not only ensure the priority of new information, but also accurately capture the nonlinear traits of system data. It also incorporates a dynamic time delay function to account for the time lag between energy and economic development, enhancing the model's flexibility. Additionally, the study combines the whale optimization algorithm and the double-error idea to optimal parameter search. The proposed model is versatile and can be simplified into 14 other grey models. The case study demonstrates the model's impressive predictive accuracy, with a fitting error of 4.02 % and a test error of 0.89 %. The model is then employed to forecast renewable energy consumption in China, predicting a rapid annual growth rate of 17.25 % from 2022 to 2030. Overall, this article successfully constructs a dynamic prediction model in theory and scientifically provides valuable data support for the nation's energy development planning in practice.