Search Optimization Algorithm Research Articles

Exploring the Human History: Cybernetic Modeling, Systemic Perspectives, and Applications in Archaeological Eras

Since the 1970s, cybernetic modeling of evolutionary processes has progressed, particularly with the development of the informatics-cybernetic model (ICM). This model conceptualizes humanity as a self-regulating hierarchical network system, continuously tracking energy-based target crite-ria through search optimization algorithms. The outcomes are recorded in the system memory of corresponding hierarchical subsystems. Within the ICM framework, the spatio-temporal charac-teristics of global evolution exhibit modifications reminiscent of the Zhirmunsky-Kuzmin number series, a geometric progression they identified in the exploration of critical levels in biosystem development. The study also showcases applications of mathematical-cybernetic modeling results in understanding historical processes examined by archaeologists and historians.

An improved cuckoo search algorithm for global optimization

An improved cuckoo search algorithm for global optimization

Enhancing CNN‐LSTM neural networks using jellyfish search algorithm for pandemic modeling

SummaryThis paper presents a comprehensive three‐step approach (CNN‐JSO‐LSTM) for predictive modeling using a pandemic such as COVID‐19 as a test case. Initially, a Convolutional Neural Network (CNN) is employed to extract crucial features pertinent to the pandemic. Subsequently, the Jellyfish Search Optimizer (JSO) algorithm is applied for feature selection, identifying the most relevant factors. These chosen features are then inputted into a Long Short‐Term Memory (LSTM) network, responsible for classifying samples into “healthy” and “diseased” categories. Our method enhances LSTM performance using the Jellyfish Search optimizer, resulting in exceptional prediction accuracy. Our experiments achieved remarkable metrics, with an accuracy of 95.32%, high sensitivity (94.87%), and precision (94.28%), surpassing alternative methods. In conclusion, our study presents a promising and highly accurate approach for pandemic prediction, harnessing deep learning and swarm intelligence techniques. These findings suggest a potential for more effective pandemic management and intervention strategies.

Slope stability prediction based on GSOEM-SV: A mobile application practicably deploy in engineering verification

Slope stability evaluation is a complex and uncertain system problem, and carrying out slope stability prediction is the prerequisite and foundation for slope disaster prevention. In order to achieve fast and accurate prediction of slope stability, this paper considers height, total slope angle, unit weight, cohesion, internal friction angle, and pore water pressure ratio as input features and proposes an intelligent slope stability prediction method based on grid search optimization ensemble learning model by soft voting (GSOEM-SV). First, 390 sets of on-site data were collected to form a dataset, and analyses including correlation coefficients, density estimates, and box lines were carried out. Then, the grid search optimization algorithm is used to optimize the hyperparameters of five algorithms—Gradient Boosting Decision Trees, Light Gradient Boosting Machine, Categorical Boosting, Support Vector Machine, and Random Forest, and integrates them through soft voting. Furthermore, this paper optimizes the hyperparameters of the above five algorithms based on grid search, particle swarm and simulated annealing algorithms, builds 15 improved models and 2 ensemble models and conducts comparison. The results reveal that the GSOEM-SV has the highest slope stability prediction accuracy, up to 91 %, the area under the curve (AUC) is 0.950, and its F1 score 0.917, which are better than the 15 improved and 2 integrated models. In addition, a set of slope stability prediction app based on uni-app is developed in the paper. It provides a technical foundation and an open and shareable information service platform for slope hazard prediction in geotechnical engineering.

Integrated machine learning for modeling bearing capacity of shallow foundations

Analyzing the stability of footings is a significant step in civil/geotechnical engineering projects. In this work, two novel predictive tools are suggested based on an artificial neural network (ANN) to analyze the bearing capacity of a footing installed on a two-layered soil mass. To this end, backtracking search algorithm (BSA) and equilibrium optimizer (EO) are employed to train the ANN for approximating the stability value (SV) of the system. After executing a set of finite element analyses, the settlement values lower/higher than 5 cm are considered to indicate the stability/failure of the system. The results demonstrated the efficiency of these algorithms in fulfilling the assigned task. In detail, the training error of the ANN (in terms of root mean square error—RMSE)) dropped from 0.3585 to 0.3165 (11.72%) and 0.2959 (17.46%) by applying the BSA and EO, respectively. Moreover, the prediction accuracy of the ANN climbed from 93.7 to 94.3% and 94.1% (in terms of area under the receiving operating characteristics curve—AUROC). A comparison between the elite complexities of these algorithms showed that the EO enjoys a larger accuracy, while BSA is a more time-effective optimizer. Lastly, an explicit mathematical formula is derived from the EO-ANN model to be conveniently used in predicting the SV.

Cascaded Fractional-Order Controller-Based Load Frequency Regulation for Diverse Multigeneration Sources Incorporated with Nuclear Power Plant

To sustain a system frequency within acceptable limits, it is widely conceded that retaining a power balance between generation and demand is necessary. In order to regulate the frequency of power systems (PSs) this article proposes a novel cascaded based fractional-order controller termed as fractional-order integer- (FOI-) fractional-order proportional integral with double derivative (FOPIDD2). In addition to redox flow batteries and capacitive energy storage, the recommended control strategy has been validated with gas, thermal reheat, hydro, and nuclear power systems. Additionally, a newly designed algorithm known as squid game optimizer (SGO) optimizes the gains of the new FOI-FOPIDD2 controller. The squid game optimizer technique is inspired by the fundamental principles of a conventional Korean sport. It employs a population of candidate solutions and iteratively adjusts the control parameters to discover the optimal set that reduces frequency abnormalities and improves system stability. A comparison is also made between the controller’s performance and benchmarks, including the jellyfish search algorithm, the firefly algorithm, the grey wolf optimizer, and the particle swarm algorithm. The proposed algorithms reduced peak overshoot as compared to grey wolf optimizer algorithm by 35.34%, 46.78%, and 76.89%; jellyfish search optimization algorithm by 34.76%, 77.22%, and 82.56%; and firefly algorithm by 82.67%, 89.23%, and 29.67% for frequency variations in area 1, area 2 and tie line power, respectively. Furthermore, SGO-FOI-FOPIDD2 controllers under different loading circumstances and conditions were evaluated and endorsed for their ability to withstand uncertainties in power system parameters.

Using the Cuckoo Optimization to Initialize the Harmony Memory in Harmony Search Algorithm to Find New Hybrid (CSHS) Algorithm

The current paper seeks to present a hybrid harmony search algorithm, using CS and HS. The proposed algorithm takes advantage of using a method to initialize the harmony memory (HM) with the help of the cuckoo search optimization algorithm. The HS algorithm is inspired by the method used by musicians to create and enhance harmony in music. It follows 3 main principles of pitch adjustment (PA), HM consideration (HMC), and random selection (RS). The performance of HS can be affected by its poor accuracy in optimization and speed of convergence, 2 main issues with it. However, to improve the HS algorithm, Cuckoo search can be used as it can do a local search using one parameter only, other than the size of the population, making it work more efficiently. This current method has been tested for its validity and efficiency in performance by being implemented on many international standard optimization problems. The results confirm the fact that the performance of the currently proposed algorithm is better and more efficient in finding solutions compared to HS and other algorithms.

Filtered Ofdm System Model for Papr Reduction in the Growth Of 5g

A proposed waveform for fifth generation (5G) communications is the filtered orthogonal frequency division multiplexing (F-OFDM) system. Filtering-based waveform frameworks distinguish themselves by suppressing out-of-band emission and asynchronous transmission. Meanwhile, the high Peak-To-Average Power Ratio (PAPR) remains a barrier to new waveform possibilities. Partials transmit sequence (PTS) is a successful strategy for reducing high PAPR in multicarrier networks. This work presents a multicarrier in OFDM and UFMC based on the Filter Bank Multicarrier (FBMC) waveform for communication systems. Furthermore, FBMC waveforms have limited orthogonality between subcarriers, which can mitigate Doppler and multipath effects. This FBMC approach uses the cosine law to filter the traffic signal, and the Welch algorithm is offered for segmentation, which lowers noise in estimated power spectra. As a result, a hybrid Selective Mapping (SLM) with Cuckoo Search and Ant Lion Optimization (CS-ALO) algorithms is suggested using the FBMC framework to reduce PAPR in the OFDM system. The proposed work is implemented, and detailed experimental research is carried out to show the increased performance in 5G networks. Based on a fair comparison, the F-OFDM scheme outperforms previous approaches in terms of BER and multicarrier system capacity. Simulation results show that the proposed waveform design and processing method outperform standard OFDM and F-OFDM methods in PAPR reduction and communication integrated systems.

Adam golden search optimization enabled DCNN for classification of breast cancer using histopathological image

Breast Cancer (BC) is a killing disorder, every year it kills millions of human beings. Early diagnosis is the only way to mitigate the mortality rate. Among all kinds of screening methods, medical imaging is an essential method for screening BC. Existing medical imaging alters the tissue structure and cell morphology. To overcome these limitations, histopathology image is used because it can support the decision of pathologists about the closeness or the non-appearance of a disease, as well as it can help in infection development estimation. Hence, this research develops an efficient method for BC classification using the proposed Adam Golden Search Optimization-based Deep Convolutional Neural Network (AGSO-DCNN). Initially, Gaussian filter-enabled pre-processing is utilized for mitigating the noises composed in the input images. Afterwards, k-means clustering is used to feed the input images into the segmentation phase to reduce the complexity of the image. Then, to extract features like shape features, statistical features, Local Vector Patterns (LVP), and Pyramid Histogram of Oriented Gradients (PHOG) feature extraction is performed. Thereafter, the obtained features are forwarded to the multi-grade BC classification stage, where DCNN is employed for classifying the image into six categories, such as apoptosis, tubule, mitosis, non-tubule, tumour nuclei, and non-tumor nuclei. DCNN is trained by the formulated AGSO mechanism, which is obtained by incorporating the Adam Optimizer and Golden Search Optimization (GSO) algorithm. Moreover, the AGSO-based DCNN technique achieved better accuracy, TPR and TNR with the values of 97.90%, 98.00%, and 98.30%, respectively.

Order Distribution and Routing Optimization for Takeout Delivery under Drone–Rider Joint Delivery Mode

Order distribution and routing optimization of takeout delivery is a challenging research topic in the field of e-commerce. In this paper, we propose a drone–rider joint delivery mode with multi-distribution center collaboration for the problems of limited-service range, unreasonable distribution, high delivery cost, and tight time windows in the takeout delivery process. The model is constructed with the minimum delivery cost and the overall maximum customer satisfaction as the objective function, and a two-stage heuristic algorithm is designed to solve the model. In the first stage, Euclidean distance is used to classify customers into the regions belonging to different distribution centers, and the affinity propagation (AP) clustering algorithm is applied to allocate orders from different distribution centers. The second stage uses an improved tabu search algorithm for route optimization based on specifying the number of rider and drone calls. This paper takes China’s Ele.me and Meituan takeout as the reference object and uses the Solomon data set for research. The experimental results show that compared with the traditional rider delivery mode, the drone–rider joint delivery mode with multiple distribution center collaboration can effectively reduce the number of riders used, lower the delivery cost, and improve the overall customer satisfaction.

Search and hunt based-driving scene perception network for distance estimation and object detection

Visual tracking is generally utilized in numerous applications including surveillance and autonomous driving to provide a wide angle of vision. However, different types of alteration, including occlusion, pose, and illumination present in the video sequence act as one of the crucial troubles considered in visual tracking. Hence, this research developed a modified Deep learning (DL) approach useful for distance estimation, object detection, and pixel-wise semantic segmentation by shared convolutional architecture. The proposed object detection model employs a modified Driving Scene Perception network (DSP), which makes use of multi-task learning to enhance the efficacy of distance evaluation, semantic segmentation, and object detection. The prime significance of the research relies on the Search and Hunt optimization (SaHO) algorithm, which optimally selects the hyperparameter value of the DSP network. The experimental analysis substantiates that the proposed modified DSP network effectively estimates the distance between the moving object and the camera. The experimental analysis depicts the effectiveness of the DSP with SaHO model for both segmentation and object detection. For object detection, the DSP with SaHO model attained high rates of 0.98, 0.94, and 0.96 for accuracy, specificity, and sensitivity respectively utilizing the cityscape dataset. Additionally, the DSP with SaHO approach attained the lowest errors for segmentation and surpassed other existing techniques that revealed the efficiency of the DSP with the SaHO model.

Multi‐mode non‐linear inversion of Rayleigh wave dispersion curves with grey wolf optimization and cuckoo search algorithm

AbstractDispersion curve inversion is one of the core contents of Rayleigh wave data processing. However, the dispersion curve inversion has the characteristics of multi‐parameter, multi‐extremum as well as nonlinearity. In the face of Rayleigh wave data processing under complex seismic‐geological conditions, it is difficult to reconstruct an underground structure quickly and accurately apply a single global‐searching non‐linear inversion algorithm. For this reason, we proposed a strategy to invert multi‐order mode Rayleigh wave dispersion curves by combining with grey wolf optimization (GWO) and cuckoo search (CS) algorithms. On the basis of introducing the mechanism of iterative chaotic map with infinite collapses (ICMIC) and the strategy of dimension learning–based hunting (DLH), an improved GWO was developed that was called IDGWO (ICMIC and DLH GWO). After searching the near‐optimal region through IDGWO, the CS with a variable step‐size Lévy flight search mechanism was switched adaptively to complete the final inversion. The correctness of our method was verified by the multi‐order mode dispersion curve inversion of a six‐layer high‐velocity interlayer model. Then it was further applied to the processing of real seismic datasets. The research results show that our method fully utilizes the advantages of each of the two global‐searching non‐linear algorithms after integrating IDGWO and CS, while effectively balancing the ability between global search and local exploitation, further improving the convergence speed and inversion accuracy and having good anti‐noise performance.

Distribution network reconfiguration utilizing the particle swarm optimization algorithm and exhaustive search methods

The load level for each period in the distribution network can be considered non-identical due to the increasing demand for loads and the bigger distribution network. The main problem in the transmission and distribution network system is power losses and voltage profiles, affecting the quality of service and operating costs. This study compares the reconfiguration of the network using exhaustive search techniques and particle swarm optimization (PSO) algorithms on the IEEE 33 bus distribution network system. The study’s results compare the study of power flow before and after network reconfiguration, which is a decrease in the value of power losses from 202.7 kW to 139.6 kW. Then voltage profile improved from 91.309% to 93.782%. The simulation results also found that this reconfiguration can improve the system voltage profile, which initially contained 21 buses outside the standard limits of IEEE Std 1159-1995 to 7 buses.

Astrophysics-based transit search optimization heuristics for parameter estimation of multi-frequency sinusoidal signals

This study investigates an astrophysics-based Transit search optimization algorithm (TSOA) for solving a challenging engineering problem. This innovative strategy uses the fundamentals of physics to improve the accuracy and efficacy of problem solving techniques. Harmonic distortions in power systems have become a massive challenge because of the nonlinear loads associated with the electrical power distribution system. Overheating of the equipment, motor failure, capacitor failure, and improper power metering are all issues caused by harmonic distortion. A new examination of the causes and consequences of these issues, as well as the status of hardware and software available for harmonic evaluation, is necessary in light of the unprecedented advancements in power electronic devices and their integration at all levels in the power and energy system. In order to estimate phase and amplitude simultaneously with a specified frequency, an objective function of power system harmonics is created. Keeping in mind the adverse effects of harmonics, parameter estimation is carried out under various conditions by taking different particle sizes and signal-to-noise ratios. TSOA proved its efficacy for both phase and amplitude parameters under different situations and precisely estimated the harmonics signal up to an accuracy of 1.1648E–15. Two harmonic signals were taken in this research work, and the best MSE values achieved are 9.748E–4, 8.287E–07, 6.157E–10, and 1.165E–15 for 30, 60, 90, and 150 dB noise, respectively, under case study 1 while varying the particle size from 50 to 450. The results for case study 2 proved to be best up to 7.373E–16, and no significant change occurred by increasing the generations above 500. The proposed study would be a step further in developing a more accurate and robust computing platform for robust estimation of harmonics arising in power and energy systems.

Accurate parameters extraction of photovoltaic models with multi-strategy gaining-sharing knowledge-based algorithm

The determination of photovoltaic (PV) model parameters has essential theoretical and practical significance for the performance evaluation, power monitoring, and power generation efficiency calculation of PV systems. In this paper, a multi-strategy gaining-sharing knowledge-based algorithm (MSGSK) is developed to determine these parameters. In our previous work, it has been demonstrated that gaining-sharing knowledge-based algorithm (GSK) is well suited for solving the concerned problem. To enhance its performance, a parameter adjustment strategy is developed to adjust the knowledge rate and knowledge ratio of GSK. Besides, a backtracking differential mutation strategy by combining the mutation scheme of differential evolution and the updating scheme of backtracking search optimization algorithm is developed to enrich the population diversity. Furthermore, a strategy selection mechanism is introduced to integrate the former two strategies to balance exploration and exploitation in different stages of the evolutionary process. The suggested MSGSK algorithm is applied to five PV cases (SDM, DDM, Photowatt-PW201, STM6-40/36, and STP6-120/36). From the experimental data, it can be observed that MSGSK extracts the PV model parameters more precisely than the basic GSK. Furthermore, it exhibits faster convergence speed and higher accuracy compared to other advanced algorithms found in the literature.

Optimizing the Efficiency of Photovoltaic Distributed Generation in the Distribution System

This article studies the influence of distributed generation (DG), specifically the influence of photovoltaic (PV) in the distribution system. The particle swarm optimization algorithm (PSO) will be applied to determine the best capacity and location of PV on a test system of EEE 33 nodes so that active power loss is minimized, and the voltage profile is improved. The performance of the applied method is evaluated by comparing its results to those from some previous methods, including the Genetic Algorithm (GA), the Bacterial Foraging Optimization Algorithm (BFOA), and the Backtracking Search Optimization Algorithm (BSOA). As a result, it proved that the proposed method is better than others in terms of processing time, voltage profile, and minimization system capacity loss. In addition, the main contribution of the study is to give detailed solutions for operators in installing how many PVs in the power system can satisfy economic and technical aspects. 

Hybrid of Meta-Heuristic Techniques Based on Cuckoo Search and Particle Swarm Optimizations for Solar PV Systems Subjected to Partially Shaded Conditions

Solar energy has a significant role in meeting rising energy demand while reducing environmental impact. Solar radiation and temperature are important factors on which PV energy production depends, but its optimal operation point is influenced by variations in the aforementioned environmental factors. The nonlinear behavior of the solar system and the variable nature of environmental conditions make determining the optimal operation point difficult. To overcome these difficulties, maximum power point tracking (MPPT) finding techniques are used to extract the optimal power from the photovoltaic energy system. The behavior of MPPT varies for different weather conditions, such as partial shading conditions (PSC), and uniform irradiance conditions. Conventional techniques are simple, quick, and efficient for tracing the MPP quickly, but they are limited to uniform weather conditions. In addition, these techniques don't achieve the Global Maxima (GM) and mostly stay stuck at the Local Maxima (LM). The Meta-Heuristic techniques aid in finding the GM, but their primary disadvantage is that they take a longer time to trace the Global Maxima. This study addresses the problem by combining Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithms, leading to a hybrid (CSPSO) technique to extract the global maximum (GM). To verify the effectiveness of the suggested technique, its performance is examined under three different irradiance patterns for different PV array configurations (such as 3S and 4S3P) through MATLAB simulation. The outcomes of CSPSO are compared with the prior well-known Meta-Heuristic techniques such as Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Crow Search Algorithm (CSA). The results show the suggested technique excels over other techniques in terms of accuracy, tracking efficiency, and tracking speed. The suggested technique is capable of tracking GMPP with an average efficiency of 99.925% and an average tracking time of 0.13 s in all shading patterns studied.

A sequence to sequence prediction model for remaining useful life of lithium-ion batteries with Bayesian optimisation process visualization

Accurate battery remaining useful life (RUL) prediction plays an important role in ensuring reliable operation of electric vehicles. In this paper, a hybrid model based on Bayesian optimization of deep convolutional neural network and long short-term memory neural network (BO-DCNN-LSTM) is proposed for battery RUL prediction. Feature extraction of raw charging characteristic curves is performed by the multilayer CNN and preliminary capacity prediction is performed by the multilayer LSTM. The model performance is explored with different training, validation and testing strategies and different prediction starting points. Validation using NASA battery aging data shows that the mean absolute error (MAE) and root mean square error (RMSE) of the RUL prediction are 0.0139 Ah and 0.0195 Ah, respectively, when the prediction starting point is the 50th cycle. In addition, this paper visualizes the process of how the Bayesian optimization (BO) algorithm searches for the global optimal combinations in the high-dimensional hyperparameter space and discusses the impact of these hyperparameters on the prediction, filling the gap in this part of the research.

Indoor positioning fingerprint database construction based on CSA-DBSCAN and RCVAE-GAN

With the increasing size of buildings, in order to achieve high-precision indoor positioning services, it is a challenging task to build an offline fingerprint database with high quality, high density and less manpower and material consumption. Aiming to solve the problem of low-quality WiFi indoor positioning fingerprint inventory constructed by traditional methods, which affects positioning accuracy and incurs high costs, this paper proposes a method for indoor positioning fingerprint database construction based on Crow Search Algorithm Optimizes Density Clustering (CSA-DBSCAN) and Regressor Conditional VAE Generative Adversarial Network (RCVAE-GAN). Collecting only a tiny amount of sparse reference point position coordinates and RSS data makes it possible to construct a high-quality WiFi indoor positioning fingerprint database. Firstly, the method utilizes the density clustering method based on Crow Search Algorithm Optimization (CSA-DBSCAN) to process RSS data collected from the reference point. This helps minimize the impact of abnormal RSS data on creating the fingerprint database. Secondly, the RCVAE-GAN depth generation model was developed. The model consists of an encoder E, a generator G, a discriminator D, and a regressor R. After constructing the model, the data with abnormal RSS will be removed and input into the model for pre-training and joint training, resulting in a high-quality deep-generation model. Finally, a high-quality and high-density fingerprint database is constructed by combining the collected reference points with fingerprint data generated by the depth generation model. Experimental results show that the proposed method reduces the root mean square error (RMSE) deviation of the generated fingerprint data by 38% and 12% respectively, compared to the RBF interpolation method and the CVAE-GAN method in the same experimental scenario. The constructed fingerprint database is used for positioning, improving positioning accuracy by 70% and 65% respectively. The method described in this paper can construct a high-quality fingerprint database, effectively improving the efficiency of fingerprint database construction and reducing the costs associated with labor and time.

Optimizing Enhanced Extended Topological Active Nets Model Using Parallel Processing

In numerous clinical applications that support the diagnosis and treatment planning of a broad variety of disorders, medical image segmentation is essential. Medical picture segmentation using the Enhanced Extended Topological Active Net (EETAN) model has proven to be successful in correctly identifying structures. This study suggests a novel way to combine the best clustering techniques and parallel processing approaches to maximize the segmentation performance of the EETAN model. The Probabilistic Depth Search Optimization (PDSO) Algorithm, which makes the parallel searching technique to find the ideal contour set, is responsible for this. This work implements parallel processing and ideal clustering to improve the EETAN model's performance in medical image segmentation. Performance metrics like accuracy, precision, recall, dice similarity, and computational time are used for a comparison study. The results demonstrate the notable enhancements attained by employing parallel processing and effective clustering.