Chicken Swarm Optimization Research Articles

A design and implementation of heart disease prediction model using data and ECG signal through hybrid clustering

ABSTRACT Detection of heart disease has become a significant topic in the medicalindustry while analyzing clinical information. Over the past years,heart disease diagnosis has been one of the emerging techniques indata mining. Besides this, developing an automated prediction systemis a challenging task, even when detecting the disease by ECG signaland data. Here, it proposes a flexible method to detect heart diseasewith the domain of both signal processing and data mining. Initially,the acquired features from the weighted feature extraction approachare fed into the hybrid clustering model; in turn, the two differentclustering results are taken to determine the finest output. Thehybrid clustering model is developed by K-Means clustering that issuperimposed with DBCAN, where the centroid and value are optimizedwith the aid of Modified Updating-based Chicken Swarm Optimization(MU-CSO). Simultaneously, the ECG signals are garnered and decomposedusing Discrete Wavelet Transform (DWT). Due to the curse ofdimensionality, the Principal Component Analysis (PCA) is deployed.Consequently, the MU-CSO-assisted hybrid clustering is employed todiagnose heart disease by ECG signal. Therefore, the efficacy of theprediction model is validated with various metrics and comparedagainst conventional methods.

Energy-efficient PEGASIS Clustering based multipath routing with Lion Swarm and Chicken swarm optimization (PEGA-LSCS) for Wireless Sensor Network

Energy-efficient PEGASIS Clustering based multipath routing with Lion Swarm and Chicken swarm optimization (PEGA-LSCS) for Wireless Sensor Network

Handcrafted Deep-Feature-Based Brain Tumor Detection and Classification Using MRI Images

An abnormal growth of cells in the brain, often known as a brain tumor, has the potential to develop into cancer. Carcinogenesis of glial cells in the brain and spinal cord is the root cause of gliomas, which are the most prevalent type of primary brain tumor. After receiving a diagnosis of glioblastoma, it is anticipated that the average patient will have a survival time of less than 14 months. Magnetic resonance imaging (MRI) is a well-known non-invasive imaging technology that can detect brain tumors and gives a variety of tissue contrasts in each imaging modality. Until recently, only neuroradiologists were capable of performing the tedious and time-consuming task of manually segmenting and analyzing structural MRI scans of brain tumors. This was because neuroradiologists have specialized training in this area. The development of comprehensive and automatic segmentation methods for brain tumors will have a significant impact on both the diagnosis and treatment of brain tumors. It is now possible to recognize tumors in photographs because of developments in computer-aided design (CAD), machine learning (ML), and deep learning (DL) approaches. The purpose of this study is to develop, through the application of MRI data, an automated model for the detection and classification of brain tumors based on deep learning (DLBTDC-MRI). Using the DLBTDC-MRI method, brain tumors can be detected and characterized at various stages of their progression. Preprocessing, segmentation, feature extraction, and classification are all included in the DLBTDC-MRI methodology that is supplied. The use of adaptive fuzzy filtering, often known as AFF, as a preprocessing technique for photos, results in less noise and higher-quality MRI scans. A method referred to as “chicken swarm optimization” (CSO) was used to segment MRI images. This method utilizes Tsallis entropy-based image segmentation to locate parts of the brain that have been injured. In addition to this, a Residual Network (ResNet) that combines handcrafted features with deep features was used to produce a meaningful collection of feature vectors. A classifier developed by combining DLBTDC-MRI and CSO can finally be used to diagnose brain tumors. To assess the enhanced performance of brain tumor categorization, a large number of simulations were run on the BRATS 2015 dataset. It would appear, based on the findings of these trials, that the DLBTDC-MRI method is superior to other contemporary procedures in many respects.

Fractional Artificial Bee Chicken Swarm Optimization technique for QoS aware virtual machine placement in cloud

SummaryCloud computing provides different constructive services in order to share huge scale information, computing resources, storage resources, and offer research knowledge. Cloud Service Providers (CSPs) afforded its services to cloud customers, usually in structure of Virtual Machines (VMs). In this paper, Fractional Artificial Bee Chicken Swarm Optimization (Fractional ABCSO) is introduced for VM placement in the cloud. The Fractional ABCSO is obtained by integrating the Fractional concept (FC), Chicken Swarm Optimization (CSO), and Artificial Bee Colony (ABC). Here, the cloud simulation is performed by means of VM and physical machine (PM). At first, VM placement is carried out using different system factors, such as Central Processing Unit (CPU), Million Instructions per Second (MIPS), bandwidth, migration cost, memory, frequency, power, along with Quality of Service (QoS). The developed Fractional ABCSO algorithm outperformed other existing techniques with regard to load, migration cost, and power consumption of 0.1614, 0.0535, and 0.0408.

A multi-objective chicken swarm optimization algorithm based on dual external archive with various elites

Multi-objective optimization problems (MOPs) that widely exist in real world concern all optimal solutions compromised among multiple objectives. Chicken swarm optimization algorithm derived from emergent behaviors of organisms provides an effective way for handling MOPs. To speed up convergence and improve uniformity of Pareto-optimal solutions, a multi-objective chicken swarm optimization algorithm based on dual external archives and boundary learning strategy (MOCSO-DABL) is proposed in this paper. Dual external archives are employed to distinguish and choose two types of elite solutions, with the purpose of more effectively guiding individual evolution. A boundary learning strategy guides the chickens to learn from boundary individuals in the later stage of evolution. Moreover, fast non-dominated sorting is adopted to establish the hierarchical social structure of a chicken population, and learning strategies of roosters, hens and chicks are improved to meet the requirements of MOPs. Experimental results on 14 benchmark functions show that the proposed MOCSO-DABL outperforms other five state-of-the-art algorithms significantly.

An Improved Chicken Swarm Optimization Algorithm Based on Adaptive Mutation Learning Strategy

<p>To solve the problem that the Chicken swarm optimization (CSO) has low solution accuracy and tends to fall into the local optimum on later stages of iteration, an adaptive mutation learning Chicken swarm optimization (AMLCSO) is proposed in this paper. Firstly, to solve the problem of uneven initial distribution and improve the algorithm’s stability, a good-point set is introduced. Secondly, according to the difference between the current individual position and the optimal individual position, the nonlinear adaptive adjustment of weight is realized and the position update step is dynamically adjusted. This strategy improves the algorithm’s convergence. Thirdly, the learning update strategies of Gaussian mutation and normal distribution are introduced to improve the probability of selection and solving accuracy and avoid falling into the local optimum. Finally, the AMLCSO is compared with other standard algorithms and improved Chicken swarm optimization algorithms on twenty benchmark test functions. The experimental results show the AMLCSO has faster convergence and higher solution accuracy.</p> <p> </p>

Research on Task Scheduling Strategy under Mobile Cloud Computing Based on ICSO

<p>With the gradual application of mobile terminals such as cell phones in production and life, mobile cloud computing has become an important part of the internet. Different from traditional cloud computing task scheduling methods, mobile cloud computing task scheduling needs to consider not only task time minimization but also the lowest possible mobile device energy consumption. We propose an improved chicken swarm optimization (ICSO) algorithm applied to the task scheduling strategy under mobile cloud computing. First, we establish a multiobjective optimization strategy with minimum completion time and minimum energy consumption. Second, for the shortcomings of the chicken swarm optimization algorithm that easily fall into local optimums leading to algorithm stagnation, we use reverse learning initialization for the chicken flock population to expand the space of understanding and an adaptive strategy for learning factors and following coefficients. To illustrate the effectiveness of our algorithm in scheduling, we chose the number of mobile devices as 50, 100, and 150 and compared the improved chicken swarm optimization algorithm, ant colony algorithm, particle swarm algorithm, and chicken swarm optimization algorithm. The results illustrate that our proposed algorithm can reduce the task completion time, control the energy consumption of mobile devices well, and save energy.</p> <p> </p>

Web page prediction using adaptive deer hunting with chicken swarm optimization based neural network model

The world wide web acts as the dominant tool for data transmission due to access such as data retrieving and data transactions. The retrieval of data from the web is a complex procedure due to the large volume of web domain. The basic uses of the websites are described through web usage mining, which mines the weblog records to identify the pattern of accessing the web pages through the user. The web page prediction assists the web users in finding the plot and obtains the information as to their requirements. Several effective algorithms have been developed to mine association rules that make the predictive model more appropriate for web prediction. They can be commonly revised to ensure the changing feature of web access patterns. The Apriori algorithm involves extracting the recurrent itemset and interrelation rule that learns the relational data is commonly utilized for web page prediction. The Apriori algorithm remains the standard model for deriving the patterns and rules from the datasets in co-operative rule extraction. The Apriori algorithm thus generates large mines associated rules for web page prediction. Hence, to select the best rule, the proposed deer hunting rooster-based chicken swarm optimization algorithm is used by integrating the cockerel search agents’ dominating social search creatures’ hunting habits and their traits of looking for food. Further, the neural network (NN) is employed in this research for the prediction of web pages with minimum error. The trained NN is a technique of unsupervised learning that analyzes a dataset of input to produce the desired result, in which the effectiveness of the NN is enhanced by optimal tuning of weight by the adaptive deer hunting rooster-based chicken swarm optimization algorithm. The experimental analysis illustrates that the proposed adaptive deer hunting rooster-based chicken swarm optimization frameworks inherit lower error measures such as mean deviation = 139.89 and symmetric mean absolute percentage error[Formula: see text]0.45579 for the FIFA dataset. The proposed web page prediction models’ L2 norm and infinity norm are 58.017 and 14, respectively, for the MSNBC_SPMF dataset.

Efficient and optimized communication in biomedical sensor networks based on bioinspired particle swarm optimization for medical applications.

Healthcare, sports, the military, location monitoring and wireless body networks are emerging as technology of major relevance. As a result of the widespread usage of biomedical sensor networks in medical applications, it is essential that data packets containing vital signs be reliably and effectively supplied to the medical center. Because of its mobility, real-time monitoring, cheap cost, and real-time feedback, it may be used in a broad variety of applications. Effective data transport and a limited energy supply are the main challenges in WBAN. Uses genetic heuristics to enhance routing algorithm efficiency. Low-energy adaptive clustering hierarchy (LEACH) and distributed energy efficiency clustering (DEC) are two kinds of clustering algorithms (DEEC). A clustering-based routing protocol may be optimized using this study's optimization approach so that the network's lifetime can be extended.. The cluster heads (CHs) in sensor nodes are picked with the least amount of overhead grading possible. The cluster is being balanced. Passive clustering based on Bioinspired Particle Swarm Optimization (BPSO) should be used for clustering purposes. Routing messages efficiently means sending them quickly and efficiently without using a lot of bandwidth. Using constraints such as distance and residual power, the optimal path for the cluster may be determined with the help of iterative and heuristic chicken swarm optimization (IHCSO) for short. An evaluation of the packet distribution allocation, capacity, and average end-to-end latency illustrates the practicability of the proposed system in research concerning its efficiency. According to the findings of the research, following the technique that was proposed leads to much better outcomes.

Data Allocation in Distributed Database based on CSO

Distributed databases (DDBs) provide smart processing of large databases, the problems of fragmentation and allocation are vital design problems in addition to the centralized design. The majority of performance degradation in DDBs is due to the communication cost by query remote access and retrieval of data. This can be optimized through an efficient data allocation approach that will provide flexible retrieval of a query by low cost accessible sites. In this paper, a novel high performance data allocation approach is designed using Chicken Swarm Optimization (CSO) algorithm. Data allocation problem (DAP) is a NP-Hard problem modelled as optimization problem. The proposed data allocation approach initially characterizes the DAP into optimal problem of choosing the appropriate and minimal communication cost provoking sites for the data fragments. Then the CSO algorithm optimally chooses the sites for each of the data fragments without creating much overhead and data route diversions. This enhances the overall distributed database design and subsequently ensures quality replication. The experimental results illustrate that the proposed CSO based intelligent data fragment allocation approach has better performance than most existing approaches and thus signifies the impact of efficient data allocation in DDBs.

Multi-Objective Optimization of Sugarcane Milling System Operations Based on a Deep Data-Driven Model

The extraction of sugarcane juice is the first step of sugar production. The optimal values of process indicators and the set values of operating parameters in this process are still determined by workers' experience, preventing adaptive adjustment of the production process. To address this issue, a multi-objective optimization framework based on a deep data-driven model is proposed to optimize the operation of sugarcane milling systems. First, the sugarcane milling process is abstracted as the interaction of material flow, energy flow, and information flow (MF-EF-IF) by introducing synergetic theory, and each flow's order parameters and state parameters are obtained. Subsequently, the state parameters of the subsystems are taken as inputs, and the order parameters-including the grinding capacity, electric consumption per ton of sugarcane, and sucrose extraction-are produced as outputs. A collaborative optimization model of the MF-EF-IF of the milling system is established by using a deep kernel extreme learning machine (DK-ELM). The established milling system model is applied for an improved multi-objective chicken swarm optimization (IMOCSO) algorithm to obtain the optimal values of the order parameters. Finally, the milling process is described as a Markov decision process (MDP) with the optimal values of the order parameters as the control objectives, and an improved deep deterministic policy gradient (DDPG) algorithm is employed to achieve the adaptive optimization of the operating parameters under different working conditions of the milling system. Computational experiments indicate that enhanced performance is achieved, with an increase of 3.2 t per hour in grinding capacity, a reduction of 660 W per ton in sugarcane electric consumption, and an increase of 0.03% in the sucrose extraction.

An Improved Chicken Swarm Optimization Algorithm for Solving Multimodal Optimization Problems.

To solve the premature convergence problem of the standard chicken swarm optimization (CSO) algorithm in dealing with multimodal optimization problems, an improved chicken swarm optimization (ICSO) algorithm is proposed by referring to the ideas of bacterial foraging algorithm (BFA) and particle swarm optimization (PSO) algorithm. First, in order to improve the depth search ability of the algorithm, considering that the chicks have the weakest optimization ability in the whole chicken swarm, the replication operation of BFA is introduced. In the role update process of the chicken swarm, the chicks are replaced by the same number of chickens with the strongest optimization ability. Then, to maintain the population diversity, the elimination-dispersal operation is introduced to disperse the chicks that have performed the replication operation to any position in the search space according to a certain probability. Finally, the PSO algorithm is integrated to improve the global optimization ability of the algorithm. The experimental results on the CEC2014 benchmark function test suite show that the proposed algorithm has good performance in most test functions, and its optimization accuracy and convergence performance are also better than BFA, artificial fish swarm algorithm (AFSA), genetic algorithm (GA), and PSO algorithm, etc. In addition, the ICSO is also utilized to solve the welded beam design problem, and the experimental results indicate that the proposed algorithm has obvious advantages over other comparison algorithms. Its disadvantage is that it is not suitable for dealing with large-scale optimization problems.

Real-time implementation of Fuzzy Logic Controller based on chicken swarm optimization for the ball and plate system

Abstract The ball and Plate (BaP) system is the typical example of the nonlinear dynamic system that is used in a wide range of engineering applications. So, many researchers in the control field are using the Bap system to check robust controllers under several points that challenge it, such as internal and external disturbances. Our manuscript proposed a position control intelligent technique with two directions (2D) for the BaP system by optimized multi Fuzzy Logic Controllers (FLC’s) with Chicken Swarm Optimization (CSO) for each one. The gains and rules of the FLC’s can tune based on the CSO. This proposal utilizes the ability of the FLC’s to observe the position of the ball. At our work, the BaP system that belonged to Control Laboratory/Systems and Control Engineering department is used for real-time proposal implementation. The results have been showing a very good percentage enhancement in settling time, rise time, and overshoot, of the X-axis and Y-axis, respectively.

Modeling of Botnet Detection Using Barnacles Mating Optimizer with Machine Learning Model for Internet of Things Environment

Owing to the development and expansion of energy-aware sensing devices and autonomous and intelligent systems, the Internet of Things (IoT) has gained remarkable growth and found uses in several day-to-day applications. However, IoT devices are highly prone to botnet attacks. To mitigate this threat, a lightweight and anomaly-based detection mechanism that can create profiles for malicious and normal actions on IoT networks could be developed. Additionally, the massive volume of data generated by IoT gadgets could be analyzed by machine learning (ML) methods. Recently, several deep learning (DL)-related mechanisms have been modeled to detect attacks on the IoT. This article designs a botnet detection model using the barnacles mating optimizer with machine learning (BND-BMOML) for the IoT environment. The presented BND-BMOML model focuses on the identification and recognition of botnets in the IoT environment. To accomplish this, the BND-BMOML model initially follows a data standardization approach. In the presented BND-BMOML model, the BMO algorithm is employed to select a useful set of features. For botnet detection, the BND-BMOML model in this study employs an Elman neural network (ENN) model. Finally, the presented BND-BMOML model uses a chicken swarm optimization (CSO) algorithm for the parameter tuning process, demonstrating the novelty of the work. The BND-BMOML method was experimentally validated using a benchmark dataset and the outcomes indicated significant improvements in performance over existing methods.

Optimal estimation of proton exchange membrane fuel cell model parameters based on an improved chicken swarm optimization algorithm

ABSTRACT Proton exchange membrane fuel cell (PEMFC) has been gradually applied in new energy vehicles, aviation and other industries, attracting widespread attention. Accurately identifying unknown parameters in the mathematical model of PEMFC is beneficial to the simulation, control and prediction of its output Current-Voltage curve. In order to identify the optimal unknown parameters, based on basic Chicken Swarm Optimization, this paper introduces positive/negative learning strategies for roosters and positive learning strategies for hens and chicks. An Improved Chicken Swarm Optimization algorithm is proposed. Compared with Particle Swarm Optimization, Salp Swarm Algorithm, Whale Optimization Algorithm and basic CSO algorithm, the proposed algorithm shows better convergence and accuracy. The five algorithms are applied to three common stacks (250W PEMFC, NedStack PS6 PEMFC, Ballard Mark V) and PEMFC monomer for model unknown parameter identification and optimization. The results show that, the ICSO algorithm obtains the minimum integral of absolute error of the actual stack voltage and the simulated stack voltage in the three test stacks and a PEMFC monomer, which are 2.288, 5.857, 2.407 and 0.408, the ICSO algorithm has a maximum increase of 8.63%, 4.52%, 6.20% and 64.83% in accuracy, respectively. The simulation data agrees well with the experimental data. These indicating that the mathematical model of PEMFC based on ICSO algorithm can accurately simulate the polarization curve at different temperatures and partial pressures, and it can be obtained that with the increase of temperature and partial pressure, the output performance of the PEMFC is also getting better.

A Proposed Framework for Face Recognition using Enhanced Local Binary Pattern Algorithm with Chinese Remainder Theorem

One of the biometric methods that have recently gained attention across the globe is Face Recognition. This was due to the availability of practicable technologies, including movable results. Several studies have been carried out on face recognition for decades, but the problem is still largely unsolved. Significant progress has been made recently in this area as a result of advancements in face modeling and analysis techniques. While a system has been developed for face recognition, the problem of high processing time is still largely unresolved. This research framework proposed an Enhanced Local Binary Pattern (ELBP) algorithm for face recognition. The Local Binary Pattern (LBP) algorithm is a method used in facial feature dimensionality reduction. Standard LBP had challenges of computational complexity. Therefore, the LBP will be enhanced with the Chinese Remainder Theorem (CRT) and will be used for feature extraction to reduce computational time, Chicken Swarm Optimization (CSO) will be used for feature selection and classification will be done using a Support Vector Machine (SVM). Performance Evaluation of the system will be done by comparing the computation time result obtained from the combination of LBP-CSO and ELBP-CSO. The ELBP-CSO is expected to have a lower computation recognition time than the LBP-CSO.

Feature Subset Selection Hybrid Deep Belief Network Based Cybersecurity Intrusion Detection Model

Intrusion detection system (IDS) has played a significant role in modern network security. A key component for constructing an effective IDS is the identification of essential features and network traffic data preprocessing to design effective classification model. This paper presents a Feature Subset Selection Hybrid Deep Belief Network based Cybersecurity Intrusion Detection (FSHDBN-CID) model. The presented FSHDBN-CID model mainly concentrates on the recognition of intrusions to accomplish cybersecurity in the network. In the presented FSHDBN-CID model, different levels of data preprocessing can be performed to transform the raw data into compatible format. For feature selection purposes, jaya optimization algorithm (JOA) is utilized which in turn reduces the computation complexity. In addition, the presented FSHDBN-CID model exploits HDBN model for classification purposes. At last, chicken swarm optimization (CSO) technique can be implemented as a hyperparameter optimizer for the HDBN method. In order to investigate the enhanced performance of the presented FSHDBN-CID method, a wide range of experiments was performed. The comparative study pointed out the improvements of the FSHDBN-CID model over other models with an accuracy of 99.57%.

Application of chicken swarm optimization algorithm for multi objective scheduling problems in FMS

Scheduling plays an important role in Flexible Manufacturing System (FMS).Various Evolutionary Algorithms are used by different researchers to solve the multi objective scheduling problems. However, solutions obtained by some of these algorithms suffer from various issues such as struck in local optima, not support deadline constraint, poor convergence speed etc. In this paper, a new optimization technique called Chicken Swarm Optimization (CSO) algorithm is implemented for optimum scheduling of Multi objective Scheduling problems considered from the literature. The Combined Objective Function (COF) is formulated by considering two objectives such as Minimization of machine idle time and Penalty cost minimization with equal weight-ages. MATLAB code has developed to determine the COF values and also for implementing CSO to obtain the optimum solution. It is observed that, the results obtained by CSO algorithm are very competitive when compared with other well-known algorithms like Genetic Algorithms (GA), Cuckoos Search Algorithm (CSA), Modified Cuckoos Search Algorithm (MCSA) & Jaya Algorithm (JA)

Optimization-Assisted CNN Model for Fault Classification and Site Location in Transmission Lines

The theme of the paper is to emphasize the detection and classification of faults and their site location in the transmission line using machine learning techniques which help to indemnify the foul-up of the humans in identifying the site and type of occurrence of fault. Moreover, the transient stability is a supreme one in power systems and so the disturbances like faults are required to be separated to preserve the transient stability. In general, the protection of the transmission line includes the installation of relays at both ends of the line that constantly monitor voltages and currents and operate unless a fault occurs on a line. Therefore, this paper intends to introduce a novel transmission line protection model by exploiting the hybrid optimization concept to train the Convolutional Neural Network (CNN). Here, the fault detection, classification and site location are diagnosed by using CNN which is trained and tested by making use of diverse synthetic field data derived from the simulation models of distinct types of transmission lines. Hence, the location and the type of faults will be predicted by the CNN depending on the fault signal characteristics which are optimally trained by a new hybrid algorithm named Chicken Swarm Insisted Spotted Hyena (CSI-SH) Algorithm that hybrids both the concept of Spotted Hyena Optimization (SHO) and Chicken Swarm Optimization (CSO). Finally, the proposed method based on CNN for fault classification and site location of transmission lines is implemented in MATLAB/Simulink and the performances are compared with various measures like classification accuracy, fault detection rate and so on.

Development of hybrid models using metaheuristic optimization techniques to predict the carbonation depth of fly ash concrete

• Intelligent prediction models of carbonation depth of fly ash concrete are developed based on machine learning techniques. • Binder content, fly-ash replacement level, water-to-binder ratio, CO 2 concentration, relative humidity and time of exposure are considered as effective influenced factors for carbonation depth. • Support vector regression combined with chicken swarm optimization algorithms achieved the best comprehensive prediction results. Carbonation is one of the utmost serious issues affecting the long-term durability of reinforced concrete. When H 2 O is present, a reaction between CO 2 gas and Ca(OH) 2 occurs, forming powdered CaCO 3 , which affects the microstructure of the concrete by lowering the pH level and causing corrosion, shortening the structure's service life. The complexity of the interaction between important parameters is difficult to capture for conventional carbonation prediction models. As a result, implementing powerful machine learning (ML) algorithms to overcome a lack of understanding of the consequences of such governing input parameters is critical. ML-based carbonation prediction models that blend four metaheuristic algorithms with Support Vector Regression (SVR) were developed to increase the accuracy and methodology of the prediction. 300 datasets from previous researches were used to develop, train, and test the SVR model. For the estimate of carbonation depth using experimental data, the possible hybrid SVR, which is made up of a Chicken Swarm Optimization (CSO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Seagull Optimization Algorithm (SOA), was used. The modelling accuracy was verified using four distinct performance indexes: Coefficient of determination (R2), Mean Square Error (MSE), Mean Absolute Error (MAE), and Variance Accounted For (VAF). The training and test sets of AI models (CSO-SVR, GWO-SVR, PSO-SVR, and SOA-SVR) exhibit a strong correlation (R2 > 0.95) between the actual and predicted carbonation depth values. The application of this model for numerical research on the parameters affecting the carbonation depth in fly-ash concrete is successful, according to this study, and it gives scientific direction for durability design.