Chicken Swarm Algorithm Research Articles

Chicken swarm algorithm with deep convolutional neural network based tongue image analysis for gastric cancer classification

Tongue images (the size, shape, and colour of tongue and the thickness, colour, and moisture content of tongue coating), reflecting the medical condition of entire body based on the model of traditional Chinese medicine (TCM) are extremely utilized in China for millions of years. Gastric cancer (GC) is great lethal kind of cancer in countries and societies. The screening and analysis of GC yet depend on gastroscopy, however its application was significantly restricted due to its invasive, maximum rate and the requirement for expert endoscopists. Early recognition in GC patients and direct treatment contribute significantly to safety for health. Consequently, this study introduces a Chicken Swarm Algorithm with Deep learningbased Tongue Image Analysis for Gastric Cancer Classification (CSADL-TIAGCC) system. The projected CSADL-TIAGCC approach studies the input tongue images for the identification and classification of GC. To accomplish this, the CSADL-TIAGCC system uses improved U-Net segmentation approach. Besides, residual network (ResNet-34) model-based feature extractor is used. Furthermore, long short term memory (LSTM) approach was exploited for GC classification and its hyperparameters are selected by the CSA. The simulation outcome of the CSADL-TIAGCC algorithm was examined under tongue image database. The experimental outcomes illustrate the enhanced results of the CSADL-TIAGCC technique with respect of different evaluation measures.

Optimized deep CNN for detection and classification of diabetic retinopathy and diabetic macular edema

Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are vision related complications prominently found in diabetic patients. The early identification of DR/DME grades facilitates the devising of an appropriate treatment plan, which ultimately prevents the probability of visual impairment in more than 90% of diabetic patients. Thereby, an automatic DR/DME grade detection approach is proposed in this work by utilizing image processing. In this work, the retinal fundus image provided as input is pre-processed using Discrete Wavelet Transform (DWT) with the aim of enhancing its visual quality. The precise detection of DR/DME is supported further with the application of suitable Artificial Neural Network (ANN) based segmentation technique. The segmented images are subsequently subjected to feature extraction using Adaptive Gabor Filter (AGF) and the feature selection using Random Forest (RF) technique. The former has excellent retinal vein recognition capability, while the latter has exceptional generalization capability. The RF approach also assists with the improvement of classification accuracy of Deep Convolutional Neural Network (CNN) classifier. Moreover, Chicken Swarm Algorithm (CSA) is used for further enhancing the classifier performance by optimizing the weights of both convolution and fully connected layer. The entire approach is validated for its accuracy in determination of grades of DR/DME using MATLAB software. The proposed DR/DME grade detection approach displays an excellent accuracy of 97.91%.

Design of Z source converter with the genetic-based chicken swarm algorithm for closed loop control of PV integrated grid

A single-phase grid coupled PV system with a switched Z-source boost converter is analysed on the basis of closed-loop control using a novel hybrid algorithm. As PV results in low-voltage DC, it is vital to step up this low-voltage DC and so a suitable DC-DC converter is needed. A switched Z-source boost converter is utilized for the step-up process as this converter uses minimum passive components, thereby reducing the cost with improved power density. Control of the converter is carried out by a closed loop control using a PI controller tuned with a novel genetic-based chicken swarm (GBCS) algorithm which combines the merits of both genetic and chicken swarm able to deal with complex problems, simple with fast convergence. The output of the converter is then given to the grid through a single-phase VSI in which DC–AC conversion takes place. While connecting PV to the grid, another factor to be taken care of is grid synchronization which is accomplished by a PI controller in which the actual and reference power are analogized. The proposed control scheme is simulated in a MATLAB environment and the outcomes are observed. The grid current THD of 1.6% and reactive power compensation is accomplished.

Optimization of track and field training methods based on SSA-BP and its effect on athletes' explosive power

Digitalization and informationization are important trends in the development of the sports industry. The study first introduced the sparrow search algorithm to improve the generalization ability of traditional neural networks, optimizing the assignment of initial weights and thresholds of neural networks; Secondly, the chicken swarm algorithm is introduced to optimize the training combination, period, and intensity of athletes based on the evaluation results, improving the subjective limitations of traditional training methods. The results of model performance analysis show that the sparrow search algorithm is better than other intelligent optimization algorithms in finding fitted parameters, and the solution error is less than 0.50. The evaluation model performs well in terms of accuracy, recall, average relative error, and R2 evaluation indicators. The model has high repeatability and is suitable for evaluating track and field training methods. The accuracy and computational speed of the chicken swarm algorithm are relatively good; Compared with other optimization models, the chicken swarm algorithm has better optimization ability and accuracy. Friedman test found significant differences in the chicken swarm algorithm, and the optimized training method has a significant positive impact on the explosive power of athletes, and the training period and intensity arrangement are reasonable and more helpful to the improvement of athletic performance. This study improves the scientific rationality of the development of track and field training methods, which is conducive to optimizing the training effect of track and field sports, and facilitates the risk management and personalized training of athletes. At the same time, it greatly promotes the integration and development of sports and computer disciplines.

Chicken swarm optimization modelling for cognitive radio networks using deep belief network-enabled spectrum sensing technique.

Cognitive radio networks (CRN) enable wireless devices to sense the radio spectrum, determine the frequency state channels, and reconfigure the communication variables to satisfy Quality of Service (QoS) needs by reducing energy utilization. In CRN, spectrum sensing is an essential process that is highly challenging and can be addressed by several traditional techniques, such as energy detection, match filtering, etc. For now, the current models' performance is impacted by the comparatively low Signal to Noise Ratio (SNR) of recognized signals and the insignificant quantity of traditional signal samples. This research proposals a new spectral sensing technique for cognitive radio networks (SST-CRN) that addresses the drawbacks of predictable energy detection models. With the use of a deep belief network (DBN), the suggested model contributes to accomplish a nonlinear threshold based on the chicken swarm algorithm (CSA). The proposed DBN enabled SST-CRN technique goes through two phases in a organized process: offline and online. Throughout the offline phase, the DBN model is methodically trained on pre-gathered data, developing the aptitude to identify problematic patterns and examples from the spectral features of the radio environment. This stage involves extensive feature extraction, validation, and model development to ensure that the DBN can professionally represent complicated spectral dynamics. Additionally, online spectrum sensing is conducted during the real communication phase to enable real-time adaptation to dynamic changes in the spectrum environment. Offline spectrum sensing is typically performed during a devoted sensing period before actual communication begins. When combined with DBN's deep learning capabilities and CSO's innate nature-inspired algorithms, a synergistic framework is created that enables CRNs to explore and allocate incidences on their own with astonishing accuracy. The proposed solution considerably improves the spectrum efficiency and resilience of CRNs by harnessing the power of DBN, which leads to more effective resource utilization and less interference. The Simulation results show that our proposed strategy produces more accurate spectrum occupancy assessments. The result parameters such as probability of detection, SNR of -24dB, the SST-CRN perfect has increased a developed Pd of 0.810, whereas the existing methods RMLSSCRN-100 and RMLSSCRN-300 have accomplished a lower Pd of 0.577 and 0.736, respectively. Our deep learning methodology uses convolutional neural networks to automatically learn and adapt to dynamic and complicated radio environments, improving accuracy and flexibility over classic spectrum sensing approaches. Future research might focus on improving CSO algorithms to better optimize the spectrum sensing process, enhancing the reliability of DBN-enabled sensing techniques.

An Efficient PV - Integrated UPQC System for Power Quality Enhancement Using Improved Chicken Swarm Optimization

In today’s world, the power distribution system's problems with Power Quality (PQ) are not unfamiliar. However, customer awareness of these issues has also risen. Likewise, there are many conventional solutions to improve the power quality. However, these traditional solutions rely on passive elements and frequently fail to respond effectively as the nature of the power system changes. Custom power devices provide adaptable solutions for many issues. Therefore, a Unified Power Quality Conditioner (UPQC) Coupled with a PV system is proposed in this paper to improve the PQ problems, including fluctuations, voltage sag and swell etc. The deployment of an Interleaved Zeta converter aids in maintaining the constant DC link voltage. In order to derive the maximum desirable power from PV system, STATCOM with Adaptive Neuro- Fuzzy Interference System (ANFIS) is used, which is fine-tuned with the help of the Improved Chicken Swarm (ICS) algorithm. In addition, UPQC with series and shunt converter is employed to decrease the current and voltage harmonics, thereby enhancing the power factor. For controlling the working of UPQC, the d-q theory-based Proportional Integral (PI) is proposed. The suggested design of PV-UPQC is evaluated and the simulation results are obtained in the MATLAB platform with a reduced THD of 2.01%.

Runoff time series prediction using LSTM dynamic neural network optimized by logistic chaotic mapping chicken swarm algorithm

Abstract An algorithm, named long short-term memory (LSTM)-logistic chaos mapping chicken swarm algorithm (LCCSA), is proposed for initializing the weights and thresholds of LSTM neural networks using the Logistic chaotic mapping chicken swarm algorithm (CSA). This algorithm aims to improve mid- to long-term runoff sequence prediction in river basins. In this model, the logistic chaotic mapping method is used to initialize the chicken swarm, and LCCSA is employed to pre-train the weights and thresholds of each layer of LSTM 50 times, using the training results' initial weights of LSTM to enhance convergence accuracy and speed. Taking the Manas river and Kuitun river, two typical basins in northern Xinjiang, China, as the research objects, LSTM-LCCSA was used to forecast the mean monthly runoff in the mid- to long-term under different lag time series by using the runoff evolution data within a certain period. The example using the basin located in northern Xinjiang demonstrates the effectiveness, stability, and generality of the LSTM-LCCSA method in mid- to long-term prediction of average monthly runoff, and the prediction accuracy and universality of LSTM-LCCSA are better than other data-driven models.

Trajectory Optimization of High-Speed Robotic Positioning with Suppressed Motion Jerk via Improved Chicken Swarm Algorithm

For the trajectory optimization of the time–jerk of robotic arms with a chicken swarm optimization algorithm, using five-order B-spline interpolation can ensure smooth and continuous acceleration, but, due to the performance problems of the algorithm, the low solution accuracy and the slow convergence speed, the ideal trajectory curve cannot be obtained. To address these problems, an improved chicken swarm algorithm based on a parallel strategy and dynamic constraints (PDCSO) is proposed, where the rooster update method is employed with a parallel strategy using X-best guidance and a Levy flight step. Dynamic constraints for the rooster are given, followed by the hens, and the optimal rooster position that improved the convergence accuracy while preventing the local optimum was determined. Simulation experiments using 18 classical test functions showed that the PDCSO algorithm outperformed other comparative algorithms in terms of convergence speed, solution accuracy and solution stability. Simulation validation in ADAMS and real machine tests proved that PDCSO can effectively reduce the running time and motion shock for robotic arms and improve the execution efficiency of such arms.

A novel single phase grid connected solar photovoltaic system for state of charge estimation using recurrent neural networks

ABSTRACT Renewable energy (RE) resources have reached the top-notch state in satisfying the energy demands of recent days. Solar photovoltaic (PV) is considered as a clean form of energy among the various RE resources. A novel single-phase grid-integrated solar PV system with Re-lift Luo converter with aid of a chicken swarm (CS) optimization algorithm is presented. The Re-lift Luo converter is implemented as it has maximum power density, extreme efficacy, high-voltage-gain ratio, and reduced switching losses. Since the PV output is intermittent, the output of the converter also gets varied, which will be maintained constant by implementing a proportional–integral (PI)-based closed loop control through the optimized CS algorithm. The normalized output is further applied to the grid via single-phase voltage source inverter. A battery is utilized to store the daytime’s solar energy and discharges to the grid during the night time. The monitoring of the battery’s state of charge (SoC) is performed by recurrent neural network (RNN). A PI controller achieves the real and reactive grid side power control. The novelty of the proposed work is the utilization of three control strategies such as PI, CS, and RNN in improving the overall performance of a grid connected system in improving the power quality. Proper control and analysis of the converters and battery through intelligent controllers are the major objectives of the paper. The outcome of the proposed concept reigns in the improvement of power quality and the proper estimation of SoC, which was supported by the detailed simulation and experimental studies. The proposed configuration is verified by simulation in MATLAB/Simulink and also tested in a real-time experimental setup, and the obtained results are as per the IEEE standards.

Nature inspired firefighter assistant by unmanned aerial vehicle (UAV) data

One of the most hazardous phenomena in forests is wildfire or bush fire and early detection of massive damage prevention is vital. Employing Unmanned Aerial Vehicles (UAV) as a visual and extinguisher tool in order to prevent this tragedy which brings fatal effects on humans and wildlife has high importance. Additionally, using aerial imagery could assist firefighters to recognize fire intensity and localize and route the fire in the forest which shrinks down casualties of firefighters. All these benefits and more is just possible by employing cheap UAVs. The proposed research uses nature-inspired image processing techniques in order to segment and classify fire in color and thermal images. Multiple nature-inspired and traditional computer vision techniques, including Chicken Swarm Algorithm (CSA) intensity adjustment (contrast enhancement), Denoising Convolutional Neural Network (DnCNN), Local Phase Quantization (LPQ) feature extraction, Bees Image Segmentation, Biogeography-Based Optimization (BBO) feature selection, Firefly Algorithm (FA) classification and more are employed to achieve high classification and segmentation accuracy. The system evaluates nine performance metrics including, F-Score, Accuracy, and Jaccard for the segmentation stage and four performance metrics for the classification stage. All experiments are conducted on the two most recent UAV fire datasets of FLAME (2021) and DeepFire (2022). Additionally, fire intensity, fire direction, and fire geometrical calculation are calculated which assists firefighters even more. As smoke shows the location of the fire, a smoke detection workflow is proposed, too. Proposed system Compared with traditional and novel methods for segmentation and classification leading to satisfactory and promising results for almost all metrics. The trained model of this system could be used in most of the current rescue UAVs in real-time applications. For the FLAME dataset (color data), segmentation precision is 95.57 % and classification accuracy is 91.33 %. Also, For the DeepFire dataset segmentation precision is 91.74 % and classification accuracy is 96.88 %.

Research on location algorithm of new energy vehicle charging station based on multi-objective decision

Abstract Under the influence of the new energy strategy, the new energy vehicle has developed vigorously. As an important supporting infrastructure, the rationality of its location construction has become the key factor for the popularization of the cars. In this paper, the influence of economy, environmental protection and convenience is comprehensively considered in the model. The comprehensive optimization objective is to minimize the annual cost of construction and operation of the stations, minimize the additional carbon emissions caused by charging, and maximize the service capacity. Considering the layout and capacity constraints of charging facilities in charging stations, the AHP is used to weight each objective function. Thus, the multi-objective optimization model can be simplified, and the chicken swarm algorithm is used to settlement this question.

Research on Dam Deformation Prediction Model Based on Optimized SVM

Although constructing a dam can bring significant economic and social benefits to a region, it can be catastrophic for the population living downstream when it breaks. Given the dynamic and nonlinear characteristics of dam deformation, the traditional dam prediction model has been unable to meet the actual engineering demands. Consequently, this paper advocates for a novel method to solve this issue. The proposed method is based on the optimization of improved chicken swarm (ICSO) and support vector machine (SVM). To begin with, the mean square error is used as the objective function, and then, we apply the improved chicken swarm algorithm to iterate continuously, and finally, the optimal SVM parameters are obtained. Through the modeling and simulation experiments of a nonlinear system, the validity of the improved chicken swarm algorithm to optimize an SVM model has been verified. Based on the horizontal displacement monitoring data of FengMan Dam, this paper analyzed the influencing factors of horizontal displacement. According to the results, three prediction models have been established, respectively: the SVM prediction model optimized by the improved chicken swarm algorithm, the SVM prediction model optimized by the basic chicken swarm algorithm and the BP neural network prediction model optimized by the genetic algorithm. The obtained results from the experiment authenticate the validity and superiority of the proposed method.

Hybrid Swarm Optimization based on Chicken and Bat for Task Scheduling in Cloud Computing

In the last few decades, the world has been evolved in the field of computing by implementing a fast, and efficient methods, and the cloud computing considered the most famous technology, that provide services to a specific application within virtualization. The significant basic mater in the environment of cloud computing referred as the task scheduling, which gain an important effect on all facilities of the cloud computing. The term of task scheduling refers to determining the best required resources to make the execution of the task happen, and multiple types of parameters should be considered in to account that have its effect on the success of the procedure including, time, cost, reliability, makespan, scalability, availability, throughput, and so on, in which the scheduling procedure should schedule the tasks to a specific virtual machine while minimizing the makespan, time, and cost. In this work a hybrid task scheduling algorithm is presented, which depends on the Bat algorithm, along with chicken swarm algorithm. For the purpose of evaluation of the hybrid algorithm, the attained results compared to the results obtained from implementing both of Bat, and Chicken swarm algorithms each alone. The experimental results illustrate that the average makespan of the hybrid algorithm is reduced relatively when comparing within the Bat, and the Chicken algorithm sequentially.

Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm

With the rapid increase of ageing infrastructures worldwide, effective and robust inspection techniques are highly demanding to evaluate structural conditions and residual lifetime. The damages on structural surfaces, for example, spalling, crack, rebar buckling and exposure, are important indicators to assess the structural condition. In fact, several state-of-the-art automated inspection techniques using these indicators have been developed to reduce human-conducted onsite inspection activities. However, the efficiency of these techniques is still required to be improved in terms of accuracy and computational cost. In this study, a vision-based crack diagnosis method is developed using deep convolutional neural network (DCNN) and enhanced chicken swarm algorithm (ECSA). A DCNN model is designed with a deep architecture, consisting of six convolutional layers, two pooling layers and three fully connected layers. To enhance the generalisation capacity of trained model, ECSA is introduced to optimize meta-parameters of the DCNN model. The model is trained and tested using image patches cropped from raw images obtained from damaged concrete samples. Finally, a comparative study on different crack detection techniques is conducted to evaluate performance of the proposed method via a group of statistical evaluation indicators.

TODA Passive location for UAV based on chicken swarm algorithm

Accurate positioning of drones is one of the key technologies for drone management and security. Aiming at the characteristics of the active emission of electronic radiation signals during the operation of UAVs, the passive positioning method based on multi-station time difference of arrival (TDOA) is studied, and the problem of solving the nonlinear equation of time difference of arrival passive location is transformed into an optimization problem of seeking the extreme value of the time difference likelihood function. The chicken swarm algorithm is used to search for the best positioning coordinates. The simulation results show that the chicken swarm algorithm can quickly and stably converge to the target position in time difference of arrival passive location system.

Multi-objective optimization of smart community integrated energy considering the utility of decision makers based on the Lévy flight improved chicken swarm algorithm

A community integrated energy system can play a key role in alleviating urban energy shortages and environmental degradation, but the presence of multiple participants and links makes the operation of such a system more complicated. How to choose the optimal energy use strategy among different decision makers is a problem that urgently needs to be solved today. Therefore, first, this paper proposes a comprehensive energy multi-objective scheduling model based on the established smart community energy management framework, which considers the utility of decision makers. From the perspective of risk, the model divides decision makers into adventurous, intermediate and conservative types in order to study the impacts of different decision makers on energy use strategies. Second, to prevent the phenomenon of the solution of the intelligent algorithm falling into the local optimum, this paper innovatively uses the Lévy flight to optimize the learning step length of the chicken swarm algorithm to quickly solve the proposed mixed integer nonlinear programming model. Finally, the model is tested through multi-scenario simulation. The results show that decision makers have an important influence on energy use strategies, and different decision makers have different energy use strategies when utility is maximized. In addition, compared with the chicken swarm algorithm, the improved algorithm increases the utility of adventurous, intermediate and conservative decision makers in type-I cases by 0.54 %, 1.2 %, and 1.5 %, respectively; and in type-II cases by 4.5 %, 4.8 %, and 3.6 %, respectively.

Artificial chicken swarm algorithm for multi-objective optimization with deep learning

With the rapid development of computer hardware in the past three decades, various classic algorithms such as neural computing and bionic optimization computing have been widely used in practical problems. This paper extended the new bionic algorithm-flock algorithm proposed in 2014 and obtained a multi-objective flock algorithm to solve the multi-objective problem. This study used aggregate functions to define social ranks, and simulated the foraging behavior of chickens in the process of searching for food in the objective space and found the balance between diversity and convergence when looking for the best Pareto solution. The algorithm took five types of bi-objective functions and four types of three-objective functions as objects and compared it with four more widely used algorithms in multi-objective problems. The results demonstrate that the MOCSO (multi-objective chicken swarm optimization) algorithm shows better results in the optimization of multi-objective problems.

Medical Equipment Comprehensive Management System Based on Cloud Computing and Internet of Things.

The continuous progress in modern medicine is not only the level of medical technology, but also various high-tech medical auxiliary equipment. With the rapid development of hospital information construction, medical equipment plays a very important role in the diagnosis, treatment, and prognosis observation of the disease. However, the continuous growth of the types and quantity of medical equipment has caused considerable difficulties in the management of hospital equipment. In order to improve the efficiency of medical equipment management in hospital, based on cloud computing and the Internet of Things, this paper develops a comprehensive management system of medical equipment and uses the improved particle swarm optimization algorithm and chicken swarm algorithm to help the system reasonably achieve dynamic task scheduling. The purpose of this paper is to develop a comprehensive intelligent management system to master the procurement, maintenance, and use of all medical equipment in the hospital, so as to maximize the scientific management of medical equipment in the hospital. Scientific Management. It is very necessary to develop a preventive maintenance plan for medical equipment. From the experimental data, it can be seen that when the system simultaneously accesses 100 simulated users online, the corresponding time for submitting the equipment maintenance application form is 1228 ms, and the accuracy rate is 99.8%. When there are 1000 simulated online users, the corresponding time for submitting the equipment maintenance application form is 5123 ms, and the correct rate is 99.4%. On the whole, the medical equipment management information system has excellent performance in stress testing. It not only predicts the initial performance requirements, but also provides a large amount of data support for equipment management and maintenance.

Research on Inter Frame Motion Optimization of Visual SLAM

The key of visual SLAM is VO (front end) algorithm. Aiming at the problems of complex calculation and low robustness of feature point method widely used in VO, an algorithm for solving camera motion parameters based on chicken swarm optimization algorithm is proposed. Based on the camera pinhole model, the relationship between the two-dimensional image coordinates of adjacent frames and the camera motion parameters is deduced. The optimization function based on Hausdorff distance target is established, and the chicken swarm algorithm is defined combined with visual slam. The fitness function of the algorithm is designed. Based on the checkerboard calibration method, the algorithm is verified by experiments.

Tree-Type Irrigation Pipe Network Planning and Design Method Using ICSO-ASV

Research on tree-type irrigation pipe networks is an important component of agricultural water-saving projects. The optimal design of tree-type irrigation pipe networks is a key aspect regarding the profitability of irrigated agriculture. Meanwhile, swarm intelligence optimization algorithms have good computational ability and can be applied to solve many optimization problems in agricultural engineering. To identify the lowest investment cost for a pipe network, this study defined the concept of an upper water node to ensure the connectivity of tree-type irrigation pipe networks, and therefore, improve the pipe network planning model without using preliminary network connection diagrams. In addition, this study proposed an improved chicken swarm optimization algorithm (Improved Chicken Swarm Optimization using Adaptive Search and Variation, ICSO-ASV), which was applied to solve 12 test functions of different dimensions. The test results show that, compared to the traditional chicken swarm algorithm and other algorithms in the control group, the ICSO-ASV algorithm could effectively improve the global search capability. Finally, the ICSO-ASV algorithm was used to plan and design 15-node and 40-node pipe networks. The calculation results show that the average investment costs of the two pipe networks generated by the ICSO-ASV algorithm were 42.20% and 31.09% lower than those generated by the traditional chicken swarm algorithm, which further verified the feasibility of applying ICSO-ASV to design tree-type irrigation pipe networks. Thus, the design method proposed in this study can solve the optimal problems of tree-type irrigation pipe networks with varying topologies. The optimal solutions can be generated automatically using the ICSO-ASV algorithm if essential parameters of the pipe network planning model are provided.