Identification of transient boundary conditions with improved cuckoo search algorithm and polynomial approximation

The cuckoo search (CS) algorithm, which is a new meta-heuristic optimization algorithm, is an efficient approach to solve continuous function optimization problems. Normally, the parameters of the CS are fixed constants. This may result in affecting the algorithm efficiency. To cope with this issue, we properly tune the parameters of the CS and propose an adaptive cuckoo search (ACS) algorithm to enhance the convergence rate and accuracy of the CS. Then, we validate the proposed ACS against test functions and compare its performance with those of particle swarm optimization (PSO), improved cuckoo search algorithm (ICS) and CS by simulation. The simulation results demonstrate that the ACS obtains some solutions better than those obtained by PSO, ICS and CS.

The differential transformation method is combined with the dual reciprocity boundary element method to solve the non-Fourier heat conduction problems in functionally gradient materials. The cuckoo search algorithm is improved by the Broyden–Fletcher–Goldfarb–Shanno algorithm to identify the boundary conditions for the heat conduction problems. The polynomial function related to coordinate and time is proposed to approximate the unknown boundary conditions. Numerical examples discuss the influences of measurement point numbers and measurement errors on inverse solutions. Numerical results demonstrate the effectiveness and accuracy of the proposed method.

In wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

Aiming at the problem of weak exploitation ability of Cuckoo Search (CS) algorithm, this paper proposes a Cuckoo Search algorithm with escape mechanism (ECS). First, we introduce a search strategy that enables candidate solution move closer to the optimal. Then, we design an escape mechanism based on backward learning. Numerical tests were conducted with the results showing a great improvement in the convergence speed and accuracy of the proposed ECS compared with the original.KeywordsCuckoo search algorithmBackward learningEscape mechanismConvergence performance

Maximum power point tracking (MPPT) control is one of the essential requirements in harnessing wind power of wind energy conversion systems (WECS). The more precise the maximum power point (MPP) is determined, the more optimal the WECS is operated. Amongst MPPT algorithms, a hill-climb search (HCS) algorithm is preferred because of its simplicity however it also has few drawbacks such as the difficulty of selecting an appropriate step size, the premature convergence phenomenon and the speed-efficiency trade-off. A cuckoo search (CS) algorithm is proposed in this paper for finding out a MPP of a WECS driven by a doubly-fed induction generator (DFIG) under various wind speeds which mostly overcomes the above disadvantages. Then, the DFIG-WECS is controlled to track the MPP. This ensures that the DFIG-WECS is always operated at MPPs regardless of various wind speeds. It is realized that the proposed CS algorithm is also a population-based algorithm inspired from the breeding behavior of cuckoos but it is quite simple, powerful and especially requires less parameters to define optimal solutions than a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm. Though numerical results, the CS algorithm shows its effectiveness in finding out MPPs in the DFIG-WECS. Furthermore, the obtained results using the CS algorithm are also compared with those using the HCS and PSO algorithms. The comparison demonstrates that the convergence value and speed of the CS algorithm are always better than those of the HCS and PSO algorithms in the MPPT application in the DFIG-WECS.

The circuit design of the CMOS based analog part of a mixed-signal integrated circuit (IC) needs a large fraction of the overall design cycle time. The automatic design of an analog circuit is inevitable, seeing recently development of System-on-Chip (SOC) design. This brings about the need to develop computer aided design (CAD) tools for automatic design of CMOS based analog circuits. In this article, a Cuckoo Search (CS) algorithm is presented for automatic design of a CMOS Miller Operational Transconductance Amplifier (OTA). The source code of the CS algorithm is developed using the C language. The Ngspice circuit simulator has been used as a fitness function creator and evaluator. A script file is written to provide an interface between the CS algorithm and the Ngspice simulator. BSIM3v3 MOSFET models with 0.18 µm and 0.35 µm CMOS technology have been used to simulate this circuit. The simulation results of this work are presented and compared with previous works reported in the literature. The experimental simulation results obtained by the CS algorithm satisfy all desired specifications for this circuit.

A cuckoo search algorithm with scale-free population topology

In this paper, the cuckoo search (CS) algorithm - neuro fuzzy controller (NFC) technique is proposed for estimating the electric and magnetic field (EMF) of over head transmission line (OHTL). In the proposed method, the CS algorithm is used to optimize the conductor spacing (arrangements) and to minimize the EMF. From the optimized arrangement of the conductors, the EMF of the transmission lines is evaluated and formed as the datasets. Then the optimized data set values are applied to the input of the NFC. Subsequently, the coordinates are given to the input of the NFC which predicts their corresponding EMF data. The proposed method is implemented in the MATLAB working platform. For testing the performance of the proposed estimation methods, two overhead transmission line models with 110 kV and 400 kV respectively are considered. In order to verify the effectiveness of the proposed method this is compared with the CS algorithm-ANN controller.

The trajectory planning time is uncertain when there is no velocity constraint in the joint space of an operating robot. This paper proposes a 3-5-3 polynomial interpolation trajectory planning algorithm based on improved cuckoo search algorithm (ICS) which functions under a velocity constraint. Kinematics analysis of the robot is conducted and the 3-5-3 polynomial interpolation function is established as the foundation for trajectory planning. The size of the step size control factor is set as a variant which varies with the number of iterations, which accelerates the algorithm’s convergence and prevents premature falling into the local optimal solution, both problems in the traditional cuckoo search algorithm where the step size control factor is fixed. The proposed algorithm was used to optimize the trajectory of a test robot with interpolation time as the search space for optimization. Any interpolation time that did not meet the speed constraint was eliminated to secure the shortest possible robot running time. The UR robot was taken as a research object to simulate the joint motion trajectory planning in MATLAB. The proposed algorithm performed well and better realized the time-optimal trajectory under the constraint of speed compared to the traditional cuckoo search algorithm, particle swarm optimization algorithm, and genetic algorithm.

This paper concerns the problem of dynamical identification for an industrial robot manipulator and presents an identification procedure based on an improved cuckoo search algorithm. Firstly, a dynamical model of a 6-DOF industrial serial robot has been derived. And a nonlinear friction model is added to describe the friction characteristic at motion reversal. Secondly, we use a cuckoo search algorithm to identify the unknown parameters. To enhance the performance of the original algorithm, both chaotic operator and emotion operator are employed to help the algorithm jump out of local optimum. Then, the proposed algorithm has been implemented on the first three joints of the ER-16 robot manipulator through an identification experiment. The results show that (1) the proposed algorithm has higher identification accuracy over the cuckoo search algorithm or particle swarm optimization algorithm and (2) compared to linear friction model the nonlinear model can describe the friction characteristic of joints better.

In this paper, a novel hybrid Invasive Weed Optimization (IWO) and Cuckoo Search (CS) algorithm (IWO/CS) is presented for phase-only pattern synthesis of large array antenna. The hybrid IWO/CS algorithm embeds the Levy flight of CS algorithm into IWO algorithm as a global guide, thereby enhancing the global search ability of the algorithm. At the same time, in order to avoid trapping into the local optimal solution, mutation operator is introduced to enhance search ability of the algorithm. Finally, competitive exclusion operation is improved to maintain the diversity of population. The hybrid algorithm is applied to phase-only beam shaping to achieve main lobe broadening and side lobe depression. Simulation results demonstrate the superiority of the hybrid IWO and CS algorithm.

A hybrid approach for optimal location and capacity of UPFC to improve the dynamic stability of the power system

The Cuckoo Search (CS) algorithm is an effective swarm intelligence optimization algorithm whose important developments were presented by Yang and Deb in 2009. The CS algorithm has been used in many applications to solve optimization problems. This paper describes an overview of the applications of CS in the scope of image processing to solve optimization problems for the image during the years 2015-2021. The main categories reviewed that used CS in the field of image processing are: image segmentation, image optimization, image noise removal, image classification, feature extraction in images, image clustering and edge detection. The aim of this paper is to provide an overview and summarize the literature review of applying CS algorithm in these categories in order to extract which categories that applied this algorithm more than others. From this review we conclude that CS was mostly applied in the image segmentation category to optimize the threshold search.

In this paper, a comparative study of different wavelet filters using improved sub-band adaptive thresholding function for denoising of satellite images, based on evolutionary algorithms, has been performed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC) and particle swarm optimisation (PSO) are used for obtaining the parameters of adaptive thresholding function required for optimum performance. The visual and quantitative results clearly show the increased efficiency and flexibility of the proposed CS algorithm based on Meyer wavelet filter over various other wavelet filters for image denoising. From the comparative study of different wavelet filters, it is found that the proposed Meyer wavelet-based CS algorithm denoising approach gives better performance in terms of signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), mean square error (MSE) and mean as compared to ABC- and PSO-based denoising approach. The proposed technique has been tested on several satellite images. The quantitative (EKI or EPI, mean, MSE, SNR and PSNR) and visual (denoised images) results show the superiority of the proposed technique over conventional and state-of-art image denoising techniques.

Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems. However, the deep learning algorithms often contain some hyper-parameters which may be continuous, integer, or mixed, and are often given based on experience but largely affect the effectiveness of activity recognition. In order to adapt to different hyper-parameter optimization problems, our improved Cuckoo Search(CS) algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm. The algorithm optimizes the hyper-parameters in the deep learning model robustly, and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal. Then, the mixed hyper-parameter in Convolutional Neural Network (CNN), Long-Short-Term Memory(LSTM) and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets. Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not, we can get a better deep learning model using our method.