Truss Structure Optimization Based on Improved Chicken Swarm Optimization Algorithm

Li, Yancang; Wang, Shiwen; Han, Muxuan

doi:10.1155/2019/6902428

Cited by 18 publications

(15 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Individuals in the CS move based on their individual rules till they discover an optimal location. Hence, the individual location in the CS corresponds to a solution to the optimization issue, and detecting an optimal solution is the best solution to the optimization issue [18]. In the entire CSO, the number of individuals in each flock is fixed to N, and the location of all CS individuals are denoted as x i,j (t) , and their meaning represents the location attained in the t-th iteration of i-th flock individual in j-th dimension.…”

Section: Design Of Csoeac Techniquementioning

confidence: 99%

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

Madhu¹,

Bharadwaj²,

Boddeda³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

In recent days, advancements in the Internet of Things (IoT) and cloud computing (CC) technologies have emerged in different application areas, particularly healthcare. The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server. Therefore, energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices. At the same time, the advent of deep learning (DL) models helps to analyze the healthcare data in the cloud server for decision making. With this motivation, this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems, called IDDM-EAC technique. The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition, clustering, and disease diagnosis. In addition, the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering (CSOEAC) technique to group the IoT devices into clusters and select cluster heads (CHs). Moreover, a new coyote optimization algorithm (COA) with deep belief network (DBN), called COA-DBN technique is employed for the disease diagnostic process. The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model. In order to inspect the betterment of the IDDM-EAC technique, a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository. The experimental results demonstrate the promising performance with the minimal total energy consumption of 63% whereas the EEPSOC, ABC, GWO, and ACO algorithms have showcased a higher total energy consumption of 69%, 78%, 83%, and 84% correspondingly.

show abstract

Section: Design Of Csoeac Techniquementioning

confidence: 99%

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

Madhu¹,

Bharadwaj²,

Boddeda³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…So, there are different locations for the 3 distinct kinds of chickens in the CS optimization. The cock attains the optimal FV in all sub-groups and it finds food effectively [22]. The location of the cock can be upgraded using the following equation.…”

Section: Hyperparameter Optimization Using Cso Algorithmmentioning

confidence: 99%

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Shoab¹,

Alotaibi²

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

With the incorporation of distributed energy systems in the electric grid, transactive energy market (TEM) has become popular in balancing the demand as well as supply adaptively over the grid. The classical grid can be updated to the smart grid by the integration of Information and Communication Technology (ICT) over the grids. The TEM allows the Peerto-Peer (P2P) energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them. At the same time, there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning (DML) models. Though some of the short term load prediction techniques have existed in the literature, there is still essential to consider the intrinsic features, parameter optimization, etc. into account. In this aspect, this study devises new deep learning enabled short term load forecasting model for P2P energy trading (DLSTLF-P2P) in TEM. The proposed model involves the design of oppositional coyote optimization algorithm (OCOA) based feature selection technique in which the OCOA is derived by the integration of oppositional based learning (OBL) concept with COA for improved convergence rate. Moreover, deep belief networks (DBN) are employed for the prediction of load in the P2P energy trading systems. In order to additional improve the predictive performance of the DBN model, a hyperparameter optimizer is introduced using chicken swarm optimization (CSO) algorithm is applied for the optimal choice of DBN parameters to improve the predictive outcome. The simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training, testing, and validation accuracy of 90.17%, 87.39%, and 87.86%.

show abstract

“…In the data mining domain, it is used to optimize the K-means clustering algorithm [32], adaptive neurofuzzy inference system [33], and feature selection problem [34]. Still, more fields have CSO been applied to, including architecture [35], transport [36], mechanical engineering [37], environmental protection [38], power [39], and robot [40].…”

Section: Optimization On Engineering Problemsmentioning

confidence: 99%

“…Qu et al [11] used adaptive distribution to replace the Gaussian distribution in the update formula of rooster in order to balance the global and local searching abilities. Li et al [12] introduced several improved factors learned from both the grey wolf optimizer (GWO) and the particle swarm optimization (PSO) to extend the searching capability. Li et al's method also integrates a duplicate remove operator to enhance the diversity of the chicken population.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

Wang

Qin

Wan

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The chicken swarm optimization (CSO) algorithm is a new swarm intelligence optimization (SIO) algorithm and has been widely used in many engineering domains. However, there are two apparent problems with the CSO algorithm, i.e., slow convergence speed and difficult to achieve global optimal solutions. Aiming at attacking these two problems of CSO, in this paper, we propose an adaptive fuzzy chicken swarm optimization (FCSO) algorithm. The proposed FCSO uses the fuzzy system to adaptively adjust the number of chickens and random factors of the CSO algorithm and achieves an optimal balance of exploitation and exploration capabilities of the algorithm. We integrate the cosine function into the FCSO to compute the position update of roosters and improve the convergence speed. We compare the FCSO with eight commonly used, state-of-the-art SIO algorithms in terms of performance in both low- and high-dimensional spaces. We also verify the FCSO algorithm with the nonparametric statistical Friedman test. The results of the experiments on the 30 black-box optimization benchmarking (BBOB) functions demonstrate that our FCSO outperforms the other SIO algorithms in both convergence speed and optimization accuracy. In order to further test the applicability of the FCSO algorithm, we apply it to four typical engineering problems with constraints on the optimization processes. The results show that the FCSO achieves better optimization accuracy over the standard CSO algorithm.

show abstract

Truss Structure Optimization Based on Improved Chicken Swarm Optimization Algorithm

Cited by 18 publications

References 62 publications

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

Contact Info

Product

Resources

About