Performance study of evolutionary algorithm for different wavelet filters for satellite image denoising using sub-band adaptive threshold

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.

Speech signal quality augmentation involves the challenge to scale down the noise and to perk up the transparency In this paper the performance of two optimization algorithms is compared for denoising the speech signal. The optimization techniques such as Artificial Bee Colony(ABC), Cuckoo Search (CS)algorithm, are used as adaptive algorithms for generating the parameters of adaptive filtering function required for optimum performance. Both techniques are analyzed for different noises at different levels. Performance of these algorithms is evaluated in terms of SNR. In the analysis it is found that CS algorithm gives 45% better result than ABC.

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.

Influence of initialization on the performance of metaheuristic optimizers

In this article, a new contrast enhancement approach is presented for quality enhancement of low-contrast satellite images. The proposed technique is based on the Artificial Bee Colony (ABC) algorithm using Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD). The method employs the ABC technique to learn the parameters of the adaptive thresholding function required for optimum enhancement. In this approach, the input image is primarily decomposed into four sub-bands through DWT, and then each sub-band of DWT is optimized through the ABC algorithm. After that, a singular value matrix of the low–low thresholded sub-band image is estimated and, finally, the enhanced image is constructed by applying inverse DWT. The results obtained through this method reveal that the proposed methodology gives better performance in terms of peak signal-to-noise ratio (PSNR), mean square error (MSE), and mean and standard deviation as compared to General Histogram Equalization (GHE), Discrete Cosine Transform and Singular Value Decomposition (DCT-SVD), DWT-SVD, Particle Swarm Optimization (PSO), and modified versions of the PSO-based enhancement approach.

A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system

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.

The main benefits of fuzzy logic control (FLC) allow a qualitative knowledge of the desired system’s behavior to be included as IF-THEN linguistic rules for the control of dynamical systems where either an analytic model is not available or is too complex due, for instance, to the presence of nonlinear terms. The computational structure requires the definition of the FLC parameters namely, membership functions (MF) and a rule base (RB) defining the desired control policy. However, the optimization of the FLC parameters is generally carried out by means of a trial and error procedure or, more recently by using metaheuristic nature-inspired algorithms, for instance, particle swarm optimization, genetic algorithms, ant colony optimization, cuckoo search, etc. In this regard, the cuckoo search (CS) algorithm as one of the most promising and relatively recent developed nature-inspired algorithms, has been used to optimize FLC parameters in a limited variety of applications to determine the optimum FLC parameters of only the MF but not to the RB, as an extensive search in the literature has shown. In this paper, an optimization procedure based on the CS algorithm is presented to optimize all the parameters of the FLC, including the RB, and it is applied to a nonlinear magnetic levitation system. Comparative simulation results are provided to validate the features improvement of such an approach which can be extended to other FLC based control systems.

A cuckoo search algorithm with scale-free population topology

A novel enhanced cuckoo search algorithm for contrast enhancement of gray scale images

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 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.

Due to the large migration of applications from traditional computing environments to cloud systems, task scheduling has become a well-known research topic. Further, assigning tasks is an essential step in the server consolidation that enables clients’ needs to be mapped to suitable resources, such as Physical Machines (PMs) or Virtual Machines (VMs). The difficulty lies in using the available shared computing resources for various tasks without degrading the quality of service (QoS) or increasing the carbon footprint. In addition, the cloud provider needs to use a good task strategy based on the requirements of the client and the IT resources capacity. The best resource utilization and quickest task execution completion times are essential metrics for a cloud provider to maintain the optimum performance of the cloud system. The appropriate solution distributes tasks to the available virtual machine while applying optimization techniques such as the artificial bee colony (ABC), the cuckoo search (CS) algorithm, and particle swarm optimization (PSO). This paper will analyze the algorithms mentioned above based on energy consumption and resource utilization.KeywordsServer consolidationCloud computingResource efficiencyMetaheuristic algorithmsEnergy consumptionPerformance parametersCloudSim

Back-propagation Neural Network (BPNN) algorithm is one of the most widely used and a popular technique to optimize the feed forward neural network training. Traditional BP algorithm has some drawbacks, such as getting stuck easily in local minima and slow speed of convergence. Nature inspired meta-heuristic algorithms provide derivative-free solution to optimize complex problems. This paper proposed a new meta-heuristic search algorithm, called cuckoo search (CS), based on cuckoo bird's behavior to train BP in achieving fast convergence rate and to avoid local minima problem. The performance of the proposed Cuckoo Search Back-Propagation (CSBP) is compared with artificial bee colony using BP algorithm, and other hybrid variants. Specifically OR and XOR datasets are used. The simulation results show that the computational efficiency of BP training process is highly enhanced when coupled with the proposed hybrid method.

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.