Hybrid Cuckoo Search Research Articles

Graphic Characterization of Terrain Using Information Computational Methods

As is well known, automated pattern recognition and satellite data analysis include a broad spectrum of pattern recognition. Image classification is a crucial remote sensing technology that facilitates the automatic understanding of massive amounts of data. Numerous researchers have put a great deal of effort into this field,and their results are impressive. In order to distinguish between diverse terrain classes, such as water, urban,rocky, vegetation, and barren, we are examining a variety of nature-inspired strategies in this article. the many methods we are using to do this, including the hybrid algorithms of Particle Swarm Optimization (PSO),Artificial Bee Colony (ABC) and Biogeography Based Optimization (BBO), Firefly and Firefly Algorithm, and Hybrid Cuckoo Search & Firefly Algorithm. These methods with a strong natural influence are crucial for applications involving distant sensing. These methods have shown good results showing increased efficiency and larger values of the Kappa coefficient, making them highly helpful for classifying aspects of the terrain. This research examines many methods of classifying satellite photos that draw inspiration from nature.

Analysis and design of optimal deep neural network model for image recognition using hybrid cuckoo search with self-adaptive particle swarm intelligence

Analysis and design of optimal deep neural network model for image recognition using hybrid cuckoo search with self-adaptive particle swarm intelligence

A multi-strategy hybrid cuckoo search algorithm with specular reflection based on a population linear decreasing strategy

A multi-strategy hybrid cuckoo search algorithm with specular reflection based on a population linear decreasing strategy

Novel Global MPPT Technique Based on Hybrid Cuckoo Search and Artificial Bee Colony under Partial-Shading Conditions

Under partial-shading conditions (PSCs), the output P-V curve of the photovoltaic array shows a multi-peak shape. This poses a challenge for traditional maximum power point tracking (MPPT) algorithms to accurately track the global maximum power point (GMPP). Single intelligent algorithms such as PSO and ABC have difficulty balancing tracking speed and tracking accuracy. Additionally, there is significant power oscillation during the tracking process. Therefore, this paper proposes a new hybrid method called the Cuckoo Search Algorithm and Artificial Bee Colony algorithm (CSA-ABC) for photovoltaic MPPT. The CSA-ABC algorithm combines the local random walk and the global levy flight mechanism of the cuckoo algorithm, by probability selection, to decide whether to group the population, and introduces adaptive weight factors and gravitational mechanisms between adjacent individuals, incorporating an algorithm restart mechanism to track new MPPs in response to changes in the external environment. The algorithm is implemented in MATLAB/Simulink using a photovoltaic power-generation system model. Simulation verification is performed under different PSC scenarios. The results show that the proposed MPPT algorithm is 6.2–78.6% faster than the PSO, CSA, and ABC algorithms and two other hybrid algorithms, with a smaller power oscillation during the tracking process and zero power oscillation during the steady process.

The hybrid algorithms in constrained optimization of the permanent magnet motors

AbstractHere, different hybrid optimization algorithms have been applied to the constrained optimization of the line‐start permanent magnet synchronous motor. The optimal results for the hybrid Cuckoo Search and hybrid Grey Wolf Optimization have been compared. In the primary objective function, the parameters describing motor functional parameters in the steady state have been included. Moreover, the non‐linear constraint function has been taken into account. The optimization procedures have been elaborated in the Delphi and Matlab environments. The mathematical model of the line‐start permanent magnet motor has been developed in the Maxwell environment. Selected optimization results are presented and discussed.

An artificial neural network analysis of the thermal distribution of a fractional-order radial porous fin influenced by an inclined magnetic field

<abstract><p>Fins and radial fins are essential elements in engineering applications, serving as critical components to optimize heat transfer and improve thermal management in a wide range of sectors. The thermal distribution within a radial porous fin was investigated in this study under steady-state conditions, with an emphasis on the impact of different factors. The introduction of an inclined magnetic field was investigated to assess the effects of convection and internal heat generation on the thermal behavior of the fin. The dimensionless form of the governing temperature equation was utilized to facilitate analysis. Numerical solutions were obtained through the implementation of the Hybrid Cuckoo Search Algorithm-based Artificial Neural Network (HCS-ANN). The Hartmann number (M) and the Convection-Conduction parameter (Nc) were utilized in the evaluation of heat transfer efficiency. Enhanced efficiency, as evidenced by decreased temperature and enhanced heat removal, was correlated with higher values of these parameters. Residual errors for both M and Nc were contained within a specified range of $ 10^{-6} $ to $ 10^{-14} $, thereby offering a quantitative assessment of the model's accuracy. As a crucial instrument for assessing the performance and dependability of predictive models, the residual analysis highlighted the impact of fractional orders on temperature fluctuations. As the Hartmann number increased, the rate of heat transfer accelerated, demonstrating the magnetic field's inhibitory effect on convection heat transport, according to the study. The complex relationship among Nc, fractional order (BETA), and temperature was underscored, which motivated additional research to improve our comprehension of the intricate physical mechanisms involved. This study enhanced the overall understanding of thermal dynamics in radial porous fins, providing significant implications for a wide array of applications, including aerospace systems and heat exchangers.</p></abstract>

A hybrid population-based algorithm for solving the Minimum Dominating Set Problem

The Minimum Dominating Sets (MDS) problem is pivotal across diverse fields like social networks and ad hoc wireless networks, representing a significant NP-complete challenge in graph theory. To tackle this, we propose a novel hybrid cuckoo search approach. While cuckoo search is renowned for its wide search space exploration, we enhance it with intensification methods and genetic crossover operators for improved performance. Comprehensive experimental evaluations against state-of-the-art techniques validate our approach's effectiveness.

Analysis of the Corneal Geometry of the Human Eye with an Artificial Neural Network

In this paper, a hybrid cuckoo search technique is combined with a single-layer neural network (BHCS-ANN) to approximate the solution to a differential equation describing the curvature shape of the cornea of the human eye. The proposed problem is transformed into an optimization problem such that the L2–error remains minimal. A single hidden layer is chosen to reduce the sink of the local minimum values. The weights in the neural network are trained with a hybrid cuckoo search algorithm to refine them so that we obtain a better approximate solution for the given problem. To show the efficacy of our method, we considered six different corneal models. For validation, the solution with Adam’s method is taken as a reference solution. The results are presented in the form of figures and tables. The obtained results are compared with the fractional order Darwinian particle swarm optimization (FO-DPSO). We determined that results obtained with BHCS-ANN outperformed the ones acquired with other numerical routines. Our findings suggest that BHCS-ANN is a better methodology for solving real-world problems.

Optimized scheme for cluster head selection and EAMPR routing protocol design in WSN

Wireless sensor networks (WSN) is a popularly emerging technology with several opportunities to sustain in various field that require multipurpose sensor nodes, less energy and non-expensive system. But in the WSN, the radio transmission needs high amount of energy and this creates the critical problem. Hence consumption of energy has to be decreased to extend the network durability. Even though there are so many techniques existing for clustering approach of WSN, they have limitations like increased energy consumption, less delivery rate of data, redundancy and unbalanced network load. Hence, these problems are solved by introducing the energy efficient deep learning techniques for clustering and finding the optimal route. Initially the initialization process of system model is performed with the implementation of energy model. In WSN, energy consumption should be reduced to enhance the QoS and balance the network traffic. Hence clustering method is used to group up the sensor nodes and the optimal cluster head is selected with the proposed technique of hybrid cuckoo search and particle swarm optimization (CSO-PSO). As the CH is chosen, the optimal path of routing data should be found in addition with the procedure of optimization and it is done through the proposed model of Optimization based routing protocol that incorporates the Energy Aware Multi Point Routing (EAMPR) protocol along with the Improved Tuna Search Optimization (ITSO) algorithm. Finally, by the use of ITSO-EAMPR technique the energy consumption will get reduced with the decrease in relative mobility and high stability of nodes would be achieved. The simulations are proceeded and the outcomes are validated. The result obtained is compared with the traditional methods to show the effectiveness of proposed technique. As per the results obtained the proposed ITSO-EAMPR attains maximized PDR and Throughput, higher energy efficiency with extension in lifetime of WSN along with decrease in BER, end-to-end latency as compared to the existing techniques.

A balanced hybrid cuckoo search algorithm for microscopic image segmentation

A balanced hybrid cuckoo search algorithm for microscopic image segmentation

Artificial Neural Network Solution for a Fractional-Order Human Skull Model Using a Hybrid Cuckoo Search Algorithm

In this study, a new fractional-order model for human skull heat conduction is tackled by using a neural network, and the results were further modified by using the hybrid cuckoo search algorithm. In order to understand the temperature distribution, we introduced memory effects into our model by using fractional time derivatives. The objective function was constructed in such a way that the L2−error remained at a minimum. The fractional order equation was then calculated by using the proposed biogeography-based hybrid cuckoo search (BHCS) algorithm to approximate the solution. When compared to earlier simulations based on integer-order models, this method enabled us to examine the fractional-order (FO) cases, as well as the integer order. The results are presented in the form of figures and tables for the different case studies. The results obtained for the various parameters were validated numerically against the available literature, where our proposed methodology showed better performance when compared to the least squares method (LSM).

Effective Segmentation and Brain Tumor Classification Using Sparse Bayesian ELM in MRI Images

Classification of tumors from MRI plays very important role for diagnosing various diseases. But, it consumes an enormous amount of time for classification. Due to the similar structure of anomalous and typical tissues in the brain, it is difficult to complete the detection process successfully. Many researchers have developed new methods for detection and classification of tumors. But most of them failed at some point due to these limitations. Therefore in our work, we introduced a new machine learning algorithm for detection and classification of tumors. In addition to this, an intellectual segmentation technique known as Improved Binomial Thresholding technique is also introduced in this paper. This newly developed approach is used to differentiate the normal and abnormal slices from brain MRI. We can extract different features from the segmented image. The extracted features may be Wavelet Transform based (WT) or Scattering Wavelet Transform based (SWT). Feature selection process is achieved using a hybrid algorithm known as CS-FS (Hybrid Cuckoo Search with Fish Swarm) to minimize the dimension of extracted features. Finally, feature classification process is performed using Sparse Bayesian Extreme Learning Machine (SBELM) classifier. The proposed method is executed with the help of BRATS (Brain Tumor Image Segmentation) 2015 dataset. The result of the proposed method is evaluated on different parameters like accuracy, specificity, and sensitivity. The values of these parameters are obtained by computing four factors such as TP, TN, FN and FP. The final evaluation results showed that, our proposed SBELM classifier has attained 97.2% accuracy, which is better than the other existing method like state-of-the-art method.

Effective Power Loss Management in the Distribution System by the Hybrid Cuckoo Search Grey Wolf Optimizer

One of the most convoluted parts of an electrical power system is its distribution system. Compared to generation and transmission losses, distribution losses are very high in the vertically integrated power system. A modern smart grid involves bidirectional power flow due to Renewable Distributed Generations (RDGs). Integration of RDGs near the load points results in a reduction in the distribution losses. This paper proposes the new Hybrid Cuckoo Search Grey Wolf Optimizer (HCSGWO) to optimally place the RDGs such as Solar Photo Voltaic and Fuel Cells and Capacitor Banks (CBs) for the improvement of voltage profiles in each bus and minimization of losses. The hybrid algorithm utilizes the advantages of both the Cuckoo Search and Grey Wolf algorithm. The proposed approach is tested with the Standard IEEE 33-bus system and the real-time TN 84-bus distribution system at all load models such as constant current, constant power, and constant impedance. The HCSGWO minimizes the system losses by optimally placing both the RDGs and CBs when compared to other algorithms. Losses are reduced by installing CBs in the real-time system and significant improvement is achieved in the voltage levels of each bus in the system.

Reposition optimization in the free-floating bike-sharing system considering transferring travels from urban rail transit

The popular free-floating bike-sharing (FFBS) system serves as a first- and last-mile solution to connect urban rail transit (URT). Recently, a number of studies have investigated how to enhance the integration between FFBS and URT. Nevertheless, how to reposition the FFBS system to promote URT ridership is still a challenge. This study proposes a bike-sharing repositioning framework to balance the demand and supply of FFBS and promote URT ridership. First, a repositioning quantity determination method based on the geographically weighted regression model is applied to determine the number of bikes to be delivered to or collected from each transfer area. Second, we establish an optimization model to minimize the total repositioning cost considering carbon price. Both the repositioning of normal bikes and recycling of broken bikes are included in this model. Subsequently, a hybrid Cuckoo Search algorithm is designed to solve the problem. The extended 2-opt, random removal and insertion algorithms are introduced to generate new solutions, which enhance the search ability. Finally, several numerical studies are conducted using data collected in Shenzhen, China, to verify the effectiveness of the proposed methods. The results demonstrate that the methodology proposed in this study is reasonable and effective. The findings are expected to guide traffic authorities to get environmental and highly utilized scheduling schemes for the integration between FFBS and URT systems.

A cloud database route scheduling method using a hybrid optimization algorithm

SummaryCloud computing has appeared as a technology allowing a company to employ computing resources such as applications, software, and hardware to calculate over the Internet. Scholars have paid great attention to cloud computing because of its cutting‐edge availability, cost decrement, and boundless applications. A cloud database is a data storage site on the web where the optimal path is spotted to access the needed database. So, placing the ideal path to a database is crucial. The cloud database defined the scheduling problem to choose the perfect route. Cloud database path scheduling is a multifaceted procedure consisting of congestion control, routing list, and network flow distribution. It has a postponement in searching for the needed source route from the cloud database. Offering numerous infinite resources with the growing database workload is an NP‐Hard optimization problem where the query request needs optimal schedules to respond to the required services. So, we have used a hybrid cuckoo search (CS) and genetic algorithm (GA), motivated by a social bird's phenomenon, to solve this problem. Integrating genetic operators has dramatically enhanced the balance between the capability of searching and utilization.

A novel hybrid cuckoo search algorithm for optimization of a line-start PM synchronous motor

A novel hybrid cuckoo search algorithm for optimization of a line-start PM synchronous motor

A novel hybrid prediction model for PM2.5 concentration based on decomposition ensemble and error correction.

PM2.5 concentration is an important index to measure the degree of air pollution. It is necessary to establish an accurate PM2.5 concentration prediction system for urban air monitoring and control. Due to the nonlinear characteristics of PM2.5 concentration, it is difficult to predict it directly. Therefore, a novel hybrid model for PM2.5 concentration based on improved variational mode decomposition (IVMD), outlier-robust extreme learning machine (ORELM) optimized by hybrid cuckoo search (CS), and chimp optimization algorithm (ChOA), error correction (EC) is proposed named IVMD-ChOACS-ORELM-EC. First of all, an improved VMD based on energy loss coefficient, named IVMD, is proposed. IVMD decomposes the original data to obtain K IMF components. Then, a hybrid optimization algorithm based on ChOA improved by CS is proposed, named ChOACS. The hybrid optimization algorithm is used to optimize ORELM. On this basis, the prediction model ChOACS-ORELM is proposed, and the K IMF components are predicted by ChOACS-ORELM. Finally, the EC model based on decomposition ensemble is established to further improve the prediction accuracy. The PM2.5 concentration data collected at hourly intervals in Beijing, Shanghai, Shenyang, and Qingdao in China are used as experimental data. The experimental results show that the correlation coefficients between the prediction results and the actual values of the four cities are 0.9999, and the prediction performance of the proposed model is better than that of all comparison models.

Analysis of delirium prediction in the ICU based on the hybrid SGDCS-ANFIS approach.

In recent years, intensive care unit (ICU) doctors have paid more attention to delirium. ICU patients have a high risk of delirium. Delirium can lead to serious adverse outcomes, but early diagnosis and prediction of delirium are very difficult and lack effective assessment tools. The causes of delirium are many and complex, and there is no definite prediction model. To solve this problem, this paper proposes a delirium prediction model based on a hybrid cuckoo search algorithm with stochastic gradient descent-the adaptive-network-based fuzzy inference system (SGDCS-ANFIS) approach. Thirty-five relevant indicators of 1072 ICU cases (536 delirium cases and 536 nondelirium cases) were selected to establish a delirium prediction model to judge whether patients tended to experience delirium. The experiments show that the delirium prediction model based on the hybrid SGDCS-ANFIS approach has better performance than traditional classification and prediction machine learning approaches, and the accuracy is improved to 73.02%. It can provide some reference for the prediction of delirium, promote early diagnosis, and provide knowledge for early intervention to improve the prognosis of ICU patients. Adding this delirium prediction model to the ICU protocol will potentially improve the treatment outcome, quality, and cost. Doctors can manage sudden symptoms more calmly, and patients will also benefit. By collecting the real-time data commonly used in electronic medical records of ICUs, the proposed delirium prediction model can be easily applied in hospitals. Delirium can lead to serious adverse outcomes, but early diagnosis and prediction of delirium are very difficult and lack effective assessment tools. We propose a hybrid SGDCS-ANFIS approach to establish delirium prediction model to judge whether ICU patients tend to experience delirium. It can provide some reference for the prediction of delirium, promote early diagnosis, and provide knowledge for early intervention to improve the prognosis of ICU patients. By collecting the real-time data commonly used in electronic medical records of ICUs, the proposed delirium prediction model can be easily applied in hospitals. Fig. Development flow from raw data to the building of the delirium prediction model and model comparison.

Cuckoo Search Algorithm: Review and its Application

Optimization techniques play a major role in real-world problems, As many cases in which decisions are made are based on random research. But choosing the optimization technology is a big challenge for the user. The cuckoo search algorithm is one of the modern optimization techniques that can replace many of the traditional techniques used, The cuckoo bird use a Levi's flight strategy based on the egg laying method to extract a solution to the problem. The presented work will provide a review of the original cuckoo search algorithm with mentioning some types of developed and hybrid cuckoo search algorithm as well as some of their applications.

Hyperspectral image classification using meta-heuristics and artificial neural network

Hyperspectral images usually comprise several continuous spectral bands that represent the category of similar objects or material within the captured scene. These high-dimensional data structures have a high level of correlation and possess unique information that can be used for precise image classification. The precise selection of useful features from these high dimensional band information is very important to reduce the challenge of hyper spectral image classification approaches. Nowadays, metaheuristic algorithms are immensely utilized as a promising tool for hyperspectral image classification. In the present research work, hyperspectral images are classified with the various combinations of meta-heuristic approaches and the neural network including the mostly used Cuckoo Search (CS) optimization algorithm to resolve the global optimization search problems considering the improvement needed in image classification. Further, the strength of CS is improved using the integration of the Genetic Algorithm (GA) fitness function within the CS. The feature selection is performed by the hybrid CS and GA algorithm and the optimized features are then fed to ANN for training and classification. The paper has shown a comparative analysis of various meta heuristics techniques with ANN on parameters like kappa coefficient, Class accuracy and overall Accuracy and the designed algorithms are tested on the Indian Pines dataset. The proposed CS and GA with ANN outperformed the two already existing works with an overall average accuracy of 97.30% and a kappa coefficient of 0.9760.