Cuckoo Search Optimization Algorithm Research Articles

An evaluation of the advantages of friction TMD over conventional TMD

The seismic performance of conventional tuned mass damper (TMD) has been often improved when more TMD mass ratio is utilized. One limitation in using higher TMD mass ratios for tall buildings is the challenges of designers from the practical point of view. So far, conventional TMD has been more uneconomical. The research on the seismic performance of friction tuned mass dampers (FTMD) is still going on. This paper aimed at evaluating the advantages of the optimal design of friction TMD over conventional TMD for tall structures. For this aim, an optimal design was developed based on a multi-objective cuckoo search optimization algorithm to find the optimal TMD and FTMD parameters, including mass, damping, frequency ratios, and the friction coefficient. Here, the seismic performances of a 40-storey tall building were evaluated and compared from structural responses and energy. Results showed that both dampers could significantly reduce the maximum floor displacement, drift, and acceleration. Furthermore, the FTMD system exhibited a better performance in reducing the roof displacement against the TMD system when the mass ratio was less than 0.03. These advantages are considered to be very important from a practical point of view.

Predicting Ground Vibrations Due to Mine Blasting Using a Novel Artificial Neural Network-Based Cuckoo Search Optimization

Blasting plays a fundamental role in rock fragmentation, and it is the first preparatory stage in the mining extraction process. However, its undesirable effects, mostly ground vibration, can cause severe damages to the surroundings, such as cracks/collapses of buildings, instability of slopes, deformation of underground space, affect underground water, to name a few. Therefore, the primary purpose of this study was to predict the intensity of ground vibration induced by mine blasting operations with high accuracy, aiming to reduce the severe damages to the surroundings. A novel artificial neural network (ANN)-based cuckoo search optimization (CSO), named as CSO–ANN model, was proposed for this aim based on 118 blasting events that were collected at a quarry mine in Vietnam. Besides, stand-alone models, such as ANN, support vector machine (SVM), tree-based ensembles, and two empirical equations (i.e., USBM and Ambraseys), were considered and developed for comparative evaluation of the performance of the proposed CSO–ANN model. Afterwards, they were tested and validated based on three blasting events in practical engineering. The results revealed that the CSO algorithm significantly improved the performance of the ANN model. In addition, the comparative results showed that the accuracy of the proposed hybrid CSO–ANN model was superior to the other models with MAE (mean absolute error) of 0.178, RMSE (root-mean-squared error) of 0.246, R2 (square of the correlation coefficient) of 0.990, VAF (variance accounted for) of 98.668, and a20-index of 1.0. Meanwhile, the other models only yielded performances in the range of 0.257–0.652 for RMSE, 0.932–0.987 for R2, 20.942–98.542 for VAF and 0.227–0.955 for a20-index. The findings also indicated that explosive charge per borehole has a special relationship with ground vibration intensity. It should be considered and used instead of total explosive charge per blast in some cases, especially for the empirical models.

WITHDRAWN: Optimized FOPID controller for power quality enhancement between feeders using interline dynamic voltage restorer

This paper proposes a novel control scheme for Interline dynamic voltage restorer (IDVR). The optimized fractional order PID (FOPID) controller is used in this work for power quality enhancement such as voltage regulation, harmonics distortion reduction, voltage sag and swells compensation, fault compensation etc. The gain parameters of FOPID controller are tuned by gravitational search algorithm (GSA). The performance of the suggested controller is evaluated by contrasting it with other optimization technique such as particle swarm optimization and cuckoo-search optimization algorithm. These simulation results represents that FOPID with GSA works better than the other techniques in terms of power quality enhancement.

DETERMINATION OF THE ELECTRONIC ENERGY LEVELS OF A QUANTUM WELL HETEROSTRUTURE Zn(1-x) MgxO/ZnO/Zn1-xMgxO.

Nowadays, Cloud Computing is the preferred choice of IT organizations as it provides dynamic and pay-per-use based services to its clients. The main problem of the cloud computing is security and privacy of data which occurs because of its open and distributed structural design. Thus, it becomes essential to design an intrusion detection system (IDS) to provide security to the system. IDS help to protect cloud ecosystem from the malicious activities of the attacker. In this research work, IDS is designed to detect malicious node by using an optimization technique along with the concept of AI (Artificial Intelligence).The Cuckoo Search (CS) algorithm is used as an optimization technique and it is a meta-heuristic approach which is inspired by the behavior of birds and CS algorithm operates on the healthiness function. After the feature optimization, different types of feature are categorized based on the node’s nature into two types namely normal and attackers. On the basis of extracted features, train the proposed system using ANN (artificial neural network) as classifier to classify the attackers which affect the networks in cloud environment. ANN is a multiclass classifier which is used to solve multi-class problems and due to this reason ANN is used in proposed work with CS optimization algorithm. In this research work, ANN is used to distinguish between attacker nodes and genuine nodes based on their optimized feature sets. Thus, instead of passing data to the attacker node, the node passes the data to the genuine node and hence, the system is protected. By using the above motioned concept in cloud computing the possibility of results improvement become high because only genuine node involving in the data transmission process. To know the performance of the system, the QoS (Quality of service) parameters such as PDR (Packet delivery ratio), energy consumption rate and total delay with and without prevention algorithm are measured. The development of proposed ARS is done in the MATLAB 2016a software with the help of various toolboxes like data acquisition, artificial intelligence, optimization and curve fitting.

Novel Fuzzy-Swarm Optimization for Sizing of Hybrid Energy Systems Applying Smart Grid Concepts

A hybrid energy system (HES) is a perfect option for supplying electric energy to remote areas. A HES normally uses renewable energy sources such as wind and PV. Owing to the intermittent nature of these sources, HES should have batteries and/or conventional energy sources. HES proposed in this study is having wind, PV, batteries, and diesel generators. The design and operation of HES are considerably improved with the use of smart grid concepts. This study introduced a fuzzy logic controller to implement a new demand response strategy (DRS) where the electricity tariff is determined based on the state of charge of the battery, the charging/discharging power from the battery, and the previous response from the customers. A modified cuckoo search (MCS) optimization algorithm is introduced for sizing HES components for the lowest cost of energy (CoE) and loss of load probability (LOLP). A multiobjective function consisting of the CoE and LOLP is used to get the optimal design of HES. The MCS reduces the number of times that the optimization algorithm executes the objective function. The continuous reduction of the swarm size proposed in this paper enhances the exploration in the beginning and enhances exploitation at the final stage. The MCS is compared with 10 state-of-the-art optimization algorithms. The results from using MCS reduced the convergence time to 25-63% of the time needed by other optimization algorithms and the DRS introduced in this study reduced the CoE by 34% compared with the flat-rate pricing.

Analyzing protein-protein interactions of coronavirus using markov clustering with cuckoo search and ant lion optimization

Proteins are complex organic compounds made up of smaller units called amino acids that are bonded together in long chains. Protein interacts with other proteins or molecules and becomes essential in the structure, function, and regulation of organisms’ cells. The Protein-Protein Interaction (PPI) results in a considerably large network. Consequently, there is a need to find a method to simplify the network for easy interpretation of the protein-protein interaction. One of the most common methods is Markov Clustering (MCL). MCL has been applied to solve graph clustering problems based on stochastic flow simulation. MCL has three main stages in the process, namely expansion, inflation, and pruning. Although MCL produces a fast and well-balanced non-hierarchical clustering, it has a limitation where the results depend on the inflation parameter being inputted manually. In this study, we develop a method to combine Markov Clustering (MCL) with Cuckoo Search (CS) and Ant Lion Optimization (ALO) Algorithm. CS and ALO are applied in MCL algorithm to obtain an optimized inflation parameter automatically. PPI network of SARS-CoV-2 and other related coronavirus datasets are used in this research and is presented in the form of a graph. The experiment shows that CS-MCL forms 47 clusters, while ALO-MCL yields 14 cluster on the PPI dataset.

COVID-19 X-ray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed distributions

Classification of COVID-19 X-ray images to determine the patient’s health condition is a critical issue these days since X-ray images provide more information about the patient’s lung status. To determine the COVID-19 case from other normal and abnormal cases, this work proposes an alternative method that extracted the informative features from X-ray images, leveraging on a new feature selection method to determine the relevant features. As such, an enhanced cuckoo search optimization algorithm (CS) is proposed using fractional-order calculus (FO) and four different heavy-tailed distributions in place of the Lévy flight to strengthen the algorithm performance during dealing with COVID-19 multi-class classification optimization task. The classification process includes three classes, called normal patients, COVID-19 infected patients, and pneumonia patients. The distributions used are Mittag-Leffler distribution, Cauchy distribution, Pareto distribution, and Weibull distribution. The proposed FO-CS variants have been validated with eighteen UCI data-sets as the first series of experiments. For the second series of experiments, two data-sets for COVID-19 X-ray images are considered. The proposed approach results have been compared with well-regarded optimization algorithms. The outcomes assess the superiority of the proposed approach for providing accurate results for UCI and COVID-19 data-sets with remarkable improvements in the convergence curves, especially with applying Weibull distribution instead of Lévy flight.

Reinforced Cuckoo Search based fugitive landfill methane emission estimation

A significant amount of fugitive methane emission is generated from Municipal Solid Waste which is quantifiable amount in the overall methane emission. Relatively several models are proposed to quantify the amount of emission in the landfill. These models are based on two methods 1. Using analytical emission 2. Measurements. In this paper, the estimation of source positions and the emissions rates of the methane emission sources are done along with the fusion of Gaussian dispersion model. The bio inspired optimization model namely reinforced cuckoo search optimization algorithm is imposed on the estimation of methane emission source positions and their emission rates. Two different case studies are carried out to prove the significance of the proposed model. Also, the Briggs model is employed to analyse the performance of proposed model when the choice of parameter differs from the actual meteorological condition. The results show the significance of proposed model both in single and multi-objective form. • Around the world, Methane (CH4) is the second most anthropogenic greenhouse gas that affects the climate and responsible for uninvited climatic change. • Methane emission from waste management is due to organic element decomposition and industrial landfills. • The Proposed Algorithm is used to estimate the position of methane emission sources and its emission rate with the help of Gaussian dispersion model. • In this paper, a bio inspired Reinforced Cuckoo Search algorithm is used to estimate the position of methane emission sources and its emission rate with the help of Gaussian dispersion model. • The results show the significance of proposed model and an extensive simulation is carried out with varied number of meteorological conditions.

CEPRAN-Cooperative Energy Efficient and Priority Based Reliable Routing Protocol with Network Coding for WBAN

The wireless body area network (WBAN) can effectively modify the health and lifestyle monitoring specifically where multiple body parameters are measured using biomedical sensor devices. However, power consumption and reliability are crucial issues in WBAN. Cooperative Communication usually prolongs the network lifetime of WBAN and allows reliable delivery of bio-medical packets. Hence, the main aim of this investigation is to propose a novel protocol Cooperative Energy efficient and Priority based Reliable routing protocol with Network coding (CEPRAN) to enhance the reliability and energy efficiency of WBAN using cooperative communication method. Firstly, to identify a relay node from the group of sensor nodes for data forwarding, an enhanced Cuckoo search optimization algorithm is proposed. Secondly, Cooperative Random Linear Network Coding approach is incorporated into the relay node to improve the packet transfer rate. CEPRAN is implemented in Ns-3 simulator and the experimental results prove that the proposed protocol outperforms the existing SIMPLE Protocol.

Shape optimization of generalized developable H-Bézier surfaces using adaptive cuckoo search algorithm

The shape optimization design of developable surface is a critical and intractable technique in CAD/CAM and used in many manufacturing planning operations. In this paper, we study shape optimization by using a new cuckoo search (CS) algorithm to promote the development of generalized developable H-Bézier surfaces (or GDHBS, for short). According to the duality between points and planes in 3D projective space, the GDHBS shape optimization problem is formulated as an arc length, energy and curvature variation minimization problem of dual curve, respectively. Then, an adaptive CS optimization algorithm is applied to the solutions of those GDHBS shape optimization models. Finally, we illustrate the effectiveness and performance of the proposed methods by some representative and convictive examples.

Trust-aware energy-efficient stable clustering approach using fuzzy type-2 Cuckoo search optimization algorithm for wireless sensor networks

With the advancement of communication and sensor technologies, it has become possible to develop low-cost circuitry to sense and transmit the state of surroundings. Wireless networks of such circuitry, namely wireless sensor networks (WSNs), can be used in a multitude of applications like healthcare, intelligent sectors, environmental sensing, and military defense. The crucial problem of WSN is the reliable exchange of data between different sensors and efficient communication with the data collection center. Clustering is the most appropriate approach to prolong the performance parameters of WSN. To overcome the limitations in clustering algorithms such as reduced cluster head (CH) lifetime; an effective CH selection algorithm, optimized routing protocol, and trust management are required to design an effective WSN solution. In this paper, a Cuckoo search optimization algorithm using a fuzzy type-2 logic-based clustering strategy is suggested to extend the level of confidence and hence network lifespan. In intra-cluster communication, a threshold-based data transmission algorithm is used and a multi-hop routing scheme for inter-cluster communication is employed to decrease dissipated energy from CHs far away from BS. Simulation outcomes indicate that the proposed strategy outperforms other communication techniques in the context of the successful elimination of malicious nodes along with energy consumption, stability period, and network lifetime.

Cuckoo Search Algorithm for Extraction Ten Parameters of Photovoltaic Models. (Dept. E.)

the important issue in the extraction of the unknown parameters in the photovoltaic model is the mathematical models. Therefore, a five equivalent circuit is analyzed in this work. The unknown parameters are five for single diode model, seven for double-diode model, eight for modified double-diode, and nine for three-diode model and ten for modified three-diode parameters. The performance of the modified three-diode model and three-diode model after estimate all parameters without assumption for any variable are compared with the performance of the both single diode model, double-diode model and also the modified double-diode of the same solar cell. Cuckoo search optimization algorithm (CSOA) is proposed for the estimation of the unknown parameters for the five solar cell equivalent models. The root mean square error, the absolute error for current and power, and the maximum power point absolute error are analyzed to the performance of the five solar cell models.

Power- and time-optimized MMSE-based joint beam-forming with relay selection for future generation MIMO networks using Modified Cuckoo-Search Optimization algorithm

Multiple Input Multiple Output (MIMO) networks operating in millimeter wave frequency band bring promising solutions for the increased demand of future generation networks in terms of data rate, signal quality, power optimization and computational complexity. A joint beam-forming (JBF) system working concurrently on source–relay–destination nodes leads to faithful delivery of signals by mitigating the effect of interferences. The traditional JBF designs in MIMO networks yield power wastage due to undesirable participation of intermediate relay nodes for message forwarding. The computational delay in beam-forming (BF) matrix update is tedious in traditional systems. This paper proposes a novel design of power-optimized JBF that facilitates optimum relay selection for solving power wastage issues. The selected relays co-operate in BF with the power constraint, and all other relays are powered down and enter into sleeping mode. Modified Cuckoo-Search Optimization (MCSO) algorithm is used for relay selection and minimum mean square error algorithm is used for BF matrix calculation. The proposed JBF is able to maximize Achievable Sum Rate (ASR) for optimum value of transmission power. The maximum power efficiency is achieved for distant communication with the aid of selected relays contributing to maximizing the ASR value. The proposed work minimizes the sum of mean square error and concurrently computes optimum time slot for BF matrix update, and hence computational delay is reduced. Thus a hybrid optimization for power and time in JBF design is achieved with relay selection and it can be widely used in future generation networks for high-quality and interference-free communication.

McCulloch’s algorithm inspired cuckoo search optimizer based mammographic image segmentation

Multi-level thresholding for mammogram image segmentation leads to much better sub-sections of the intensity span, and hence very useful in breast cancer detection. In order to segment the mammogram image efficiently, in this paper, three popular nature inspired algorithms namely Harmony Search Algorithm (HSA), Electro-magnetism Optimization (EMO) and McCulloch’s Algorithm inspired Cuckoo Search Optimization algorithm (MACSO) are studied in detail; and are employed for desired cost function maximization for two well-known multi-level thresholding methods like Otsu and Kapur efficiently. The proposed approach is applied to all the 322 test images of database presented by Mammographic Image Analysis Society (MIAS), to detect pectoral muscle, breast and suspicious mass efficiently. Performance of EMO, MACSO and HSA were analysed using measures like best fitness, MSE, PSNR, SSIM and TIME. From the experimental results, it is concluded that MACSO with Otsu was found to be robust for segmentation of mammogram images accurately.

An efficient online sequential extreme learning machine model based on feature selection and parameter optimization using cuckoo search algorithm for multi-step wind speed forecasting

Accurate wind speed forecasting (WSF) has become increasingly important to overcome the adverse effects of stochastic nature of the wind on wind power generation. This paper proposes a multi-step hybrid online WSF model by combining online sequential extreme learning machine (OSELM), optimized variational mode decomposition (OVMD) and cuckoo search optimization algorithm (CSO). OVMD decomposes the wind speed series into subseries, and CSO selects the input features for each subseries. Multi-step forecasting for each subseries is performed using OSELM model optimized by CSO. Finally, the forecasting results are obtained by the aggregate calculations. The proposed model has been examined by using 10-min average wind speed data collected in monsoon and winter seasons from a supervisory control and data acquisition system of a 1.5 MW wind turbine situated in central dry zone of Karnataka, India. The results reveal that the model proposed captures the nonlinear characteristics of the wind speed in a better manner in comparison with the batch learning approach, giving accurate wind speed forecasts. This can help wind farms to estimate the wind power in a location efficiently.

RETRACTED ARTICLE: Robust image watermarking using fractional Krawtchouk transform with optimization

Fractional Krawtchouk Transform is a generalization of the Krawtchouk transforms which has two fractional orders. By adjusting these fractional orders in the weighted two dimensional Krawtchouk polynomials, local image features can be located. This paper proposes a robust image watermarking method using FrKT with firefly and cuckoo search optimization algorithms. The frequency domain image is obtained by applying FrKT for the input image blocks. The optimal fractional parameters of the transform improve the imperceptibility of the secret data in the host images. The fractional parameter selection for the image transformation is performed by Firefly optimization algorithm. Also, the optimal location in each block to hide the secret data is identified by the cuckoo search algorithm. The histogram shifting technique is used to embed the secret data in the optimal locations due to its less computational complexity. The parameters like Peak Signal to Noise Ratio, Normalized Correlation Coefficient, Structural SIMilarity index, Bit Error Rate are used for comparison of the proposed method using the optimization algorithms. The experimental results of the proposed method FrKT with combination of both Firefly and cuckoo search optimization shows better quality of the watermark, robustness and imperceptibility against various attacks. It can be concluded that the proposed FrKT + CS + FA provides an average of 0.92 for most of the attacks that prove the robustness of the proposed scheme.

Placement of Distributed Generation and Shunt Capacitor in Distribution Network using Cuckoo Search Algorithm

This work aims at reduction in active and reactive power loss reduction in distribution networks as well as to improve the voltage stability of the networks. Optimum Distributed Generation (DG) placement and sizing is carried out in conjunction with shunt capacitor placement and sizing to determine the appropriate sizes of DG units and Capacitor banks to be placed in the networks so as not to violate certain constraints. The optimal sizes of the DG units and capacitor banks were obtained on application of a Cuckoo Search Optimization Algorithm while computations for Voltage stability was performed using the Voltage Stability Index (VSI). The obtained optimal sizes of DG units and Capacitors were individually and simultaneously placed on the distribution networks to ascertain the behaviour of the networks prior to and after their placements. The performance factors considered are power loss and voltage stability. A comparison of these performance factors under separate and simultaneous placement method was demonstrated using IEEE 33 and 69 test buses. Result show that power loss (active and reactive) reduced by 63.29% and 59.38% respectively for 33 bus system, with a 74.29% and 79.19% reduction on 69 bus system. Voltage stability also increased by 7.89% and 3.79% respectively for 33 and 69 bus system relative to values obtained for base case and separate DG and shunt capacitor placement.
 Keywords: Distributed generation, shunt capacitor, Cuckoo Search Algorithm (CSA), power loss and voltage stability.

Soft-sensor modeling for l-lysine fermentation process based on hybrid ICS-MLSSVM

The l-lysine fermentation process is a complex, nonlinear, dynamic biochemical reaction process with multiple inputs and multiple outputs. There is a complex nonlinear dynamic relationship between each state variable. Some key variables in the fermentation process that directly reflect the quality of the fermentation cannot be measured online in real-time which greatly limits the application of advanced control technology in biochemical processes. This work introduces a hybrid ICS-MLSSVM soft-sensor modeling method to realize the online detection of key biochemical variables (cell concentration, substrate concentration, product concentration) of the l-lysine fermentation process. First of all, a multi-output least squares support vector machine regressor (MLSSVM) model is constructed based on the multi-input and multi-output characteristics of l-lysine fermentation process. Then, important parameters (gamma, lambda, sigma) of MLSSVM model are optimized by using the Improved Cuckoo Search (ICS) optimization algorithm. In the end, the hybrid ICS-MLSSVM soft-sensor model is developed by using optimized model parameter values, and the key biochemical variables of the l-lysine fermentation process are realized online. The simulation results confirm that the proposed regression model can accurately predict the key biochemical variables. Furthermore, the hybrid ICS-MLSSVM soft-sensor model is better than the MLSSVM soft-sensor model based on standard CS (CS-MLSSVM), particle swarm optimization (PSO) algorithm (PSO-MLSSVM) and genetic algorithm (GA-MLSSVM) in prediction accuracy and adaptability.

Trajectory tracking control based on non-singular fractional derivatives for the PUMA 560 robot arm

In this paper, a novel hybrid fractional-order control strategy for the PUMA-560 robot manipulator is developed and presented, which combines the derivative of Caputo–Fabrizio and the integral of Atangana–Baleanu, both in the Caputo sense. The fractional-order dynamic model of the system (FODM) is also considered which consists of two models, the robot manipulator model, and the model of the induction motors which are the actuators that drive their joints. The fractional model of the manipulator is obtained using the Euler–Lagrange formulation. On the other hand, for controlling each one of the induction motors, fractional-order controllers ${{\mathop{\mathrm{PI}}\nolimits } ^{\vartheta }}$ based on Atangana–Baleanu in the Caputo sense integral were developed. And for the trajectory tracking control, fractional-order controllers ${{\mathop{\mathrm{PD}}\nolimits } ^{\xi }}$ were developed based on the fractional derivative of Caputo–Fabrizio in the Caputo sense. Also, ordinary PI and PD controllers were developed for the PUMA robot control to compare their performance with the fractional-order controllers. The results obtained demonstrated that the fractional-order controllers have a better capability for tracking trajectory tasks than the integer-order controllers, even when changes of the desired trajectory and external disturbances are considered. Additionally, an end-effector trajectory tracking task for manufacturing applications is also considered. All numerical simulations were performed by using the same orders and gains, demonstrating that the proposed fractional-order ${{\mathop{\mathrm{PI}}\nolimits } ^{\vartheta }}$ and ${{\mathop{\mathrm{PD}}\nolimits } ^{\xi }}$ controllers are robust, under different operating conditions, for tracking trajectory tasks. The fractional-order controllers and the integer-order controllers were tuned applying the cuckoo search optimization algorithm where the root-mean-square error (RMSE) was chosen as the cost function to minimize.

Current Control Optimization for Grid-tied Inverters Using Cuckoo Search Algorithm

One of the most decisive factors for a smooth and stable operation of an DC / AC converter connected to the power grid are the gains used in the current controllers. This paper proposes the use of the Cuckoo Search optimization algorithm via Lévy Flights to facilitate the determination of the optimal gains of the grid connected DC/ AC converters. With the proposed algorithm, it becomes possible to determine the optimal gains of the current controllers of the DC / AC converters connected with the grid thus improving their stability, accuracy and response time.
 Keywords: DC/AC converters, Cuckoo search, Optimization, Current controllers