Chaotic Grey Wolf Optimization Research Articles

A chaotic grey wolf optimizer for constrained optimization problems

AbstractBio‐inspired algorithms have become popular due to their capability of finding good solutions for complex optimization problems in an acceptable computational time. The Grey Wolf Optimizer is a nature‐inspired, population‐based metaheuristic that simulates the social hierarchy and the hunting strategy observed in a grey wolf pack. Although the Grey Wolf Optimizer has been successfully applied to solve different optimization problems, it may suffer from premature convergence and get stuck in local optima. In order to overcome these drawbacks, this paper proposes a chaotic version of the Grey Wolf Optimizer that differs from the original algorithm and previously published modified versions because it does not add a chaotic variable in the parameters that control the execution of the algorithm. Instead, the proposed model uses a chaotic variable to define the wolves in the pack that will be used to guide the hunting process in each iteration of the algorithm. Numerical experiments using 20 benchmark functions are carried out. The performance of the proposed model is compared with the performance of the original Grey Wolf Optimizer and other well‐known algorithms, namely the Particle Swarm Optimization, the Genetic Algorithm, the Symbiotic Organisms Search, and the Teaching‐Learning Based Optimization. Nine chaotic maps reported in the literature are tested. The results show that the proposed algorithm has a very competitive performance, and the Chebyshev map presented the best performance among the chaotic maps simulated. The proposed algorithm can be integrated into other modified versions of the Grey Wolf Optimizer in a straightforward way.

Parameters identification of photovoltaic cells using improved version of the chaotic grey wolf optimizer

Precise estimation of critical parameters of the photovoltaic models is a highly demanding task in modeling and simulation of the photovoltaic systems. In this paper, a new enhanced optimization method is proposed to estimate the unknown parameters of photovoltaic modules. The proposed adaptive chaotic grey wolf optimization (ACGWO) algorithm is employed to estimate the parameters of solar cells models for single-diode, double-diode, three-diode. The suggested optimization method is obtained by combining the adaptive grey wolf optimization (AGWO) and chaotic grey wolf optimization (CGWO) algorithms. Minimization of Root Mean Squared Error (RMSE) as employed as the common objective function. Besides, the results are compared with some well-known algorithms. The results of RMSE are compared with commonly used objective functions such as the sum of squared error (SSE) and Maximal Absolute Error (MAE). The efficiency of the ACGWO method is analyzed, and the results of the simulation report the lowest values for RMSE and confirm accuracy, robustness, and high convergence speed in comparison with some well-known optimization methods. According to the results, the proposed ACGWO could outperform four other methods competitive based on RMSE values, and the best values are reported for the ACGWO method.

An intelligent grid synchronization technique for microgrid system with smooth power quality using chaotic grey wolf optimization‐random forest algorithm scheme

This manuscript proposes an intelligent grid synchronization technique for microgrid system with smooth power quality using proposed approach. The proposed approach is the consolidation of chaotic grey wolf optimization (CGWO) and random forest algorithm (RFA), hence it is called CGWO-RFA approach. The major purpose of this work is to efficiently differentiate the phase angle utility signal, monitor the phase with smooth frequency varieties, take out disruptions with harmonics, and consider the power to load uninterruptedly. In the proposed approach, the CGWO method is utilized to set the optimum control signals of the converter in offline mode depending on the power variation between the source and load side. Similarly, the limits of the proposed technique are voltage variation, frequency, and phase angle between the associated phases of the generator output and grid supply. The RFA predicts the optimal control signals depending on the accomplished dataset in the online way. The proposed CGWO-RFA method is executed in MATLAB/Simulink work site and the performance is analyzed with different existing methods, like CGWOANN, DSOGIPLL, DDSRFPLL, and DSOGIFLL. Moreover, the statistical analysis, GMPPT searching duration, and the brief comparison of synchronization scheme for explicit applications are likewise dissected and analyzed. Here, the GMPPT searching duration of the proposed technique is 0.413 seconds. The experimental outcomes demonstrate that the CGWO-RFA technique has high accuracy, high response, and robust to the faulty conditions.

A Novel Adaptive Fault Diagnosis Method for Wind Power Gearbox

In the noisy environment, fault characteristics of the composite faults of the wind power gearbox are coupled with each other, which makes the extraction features more difficult. In order to extract the characteristics of composite faults, a new fault diagnosis method for wind power gearbox is proposed in this paper, namely the modified Savitzky Golay Laplacian of Gaussian filter (MSGloG). The method can not only solve the defects that the scale parameters of the Modified Laplacian of Gaussian filter (MloG) filter are not adaptive, but also overcome the problems that the smoothing effect is too much affected by noise. Firstly, determining the Laplace model of Gaussian filter, and using the least square convolution smoothing process to improve the signal-to-noise ratio of the vibration signal. Secondly, a new marginal envelope spectrum entropy index is proposed to measure the complex fault characteristics. Finally, a new chaotic grey wolf optimization algorithm is proposed, which uses the marginal envelope spectral entropy as the fitness function, and the purpose is to make the MSGloG noise reduction adaptive. The method extracted the faults of the bearing outer ring and rolling elements successfully.

Optimized Dynamic Stochastic Resonance framework for enhancement of structural details of satellite images

Image enhancement is an essential tool for increasing the contrast of an image to visualize the dark and bright areas. The enhancement algorithms are very much relevant in remote sensing applications as the satellite images are normally of poor contrast. The dynamic stochastic resonance (DSR) attains the enhancement of poor contrast and low illuminated images by utilizing the internal noise. The conventional DSR method employed for enhancing the dark images demands proper tuning of bistable element parameters and appropriate transform domain which are found to be challenging. In this paper, we propose chaotic grey wolf optimizer to attain the optimized parameters of dynamic stochastic resonance in non-sub sampled shearlet transform domain (NSST) to enhance the low contrast satellite images. In addition, we have tested the proposed method on a variety of satellite images captured by different sensors of local cities and global areas. The quality of the proposed method is compared with that of recent enhancement algorithms. The proposed method demonstrates to be the most reliable in enhancing the image structure contrast while preserving the true colors of satellite images. The source code and dataset is available in https://github.com/shyamfec/ODSRF.

An efficient parameter optimization of software reliability growth model by using chaotic grey wolf optimization algorithm

Software reliability growth model (SRGM) with modified testing-effort function (TEF) is a function to evaluate and foresee the parameters of the data. Reliability of software is portrayed as the distinct possibility that for a predefined time, a software package will continue to run on an advance domain without frustration. SRGM utilized a few optimization procedure algorithms to advance the parameters by bifurcating them into a few stages however to upgrade the technique by using all of the parameters at the same time, the algorithm utilized is the chaotic grey wolf optimization algorithm (CGWO). CGWO is an advanced heuristic system for portraying the execution by achieving complex parameter optimization and designing application issues. Different parametric reliabilities rely upon the attributes or characteristics of the data. The parameters are predicted using the Pham–Zhang (PZ) model. Tandem computer software dataset DS1 and DS2 are used to compare the predicted parameter of SRGM obtained by Pham–Zhang (PZ) model using testing effort functions (TEFs) based on the evaluation metrics mean square error (MSE), relative error (RE) and coefficient of determination (R2). To enhance the reliability of SRGM, the parameters of SRGM estimated using TEF and enhanced using chaotic maps to improve search performance. By using the constrained benchmark functions the results of chaotic maps are obtained. Based on the chaotic graph results, the Chebyshev graph shows a good convergence rate of 78%. Overall, 86% of the results revealed an association between the choice variable and fitness criteria for CGWO. In the SRGM using CGWO, the expected result is completely mechanized and does not require any client necessity.

Analysis of high-dimensional genomic data using MapReduce based probabilistic neural network

Background: The size of genomics data has been growing rapidly over the last decade. However, the conventional data analysis techniques are incapable of processing this huge amount of data. For the efficient processing of high dimensional datasets, it is essential to develop some new parallel methods.Methods: In this work, a novel distributed method is presented using Map-Reduce (MR)-based approach. The proposed algorithm consists of MR-based Fisher score (mrFScore), MR-based ReliefF (mrRefiefF), and MR-based probabilistic neural network (mrPNN) using a weighted chaotic grey wolf optimization technique (WCGWO). Here, mrFScore, and mrRefiefF methods are introduced for feature selection (FS), and mrPNN is implemented as an effective method for microarray classification. The proper choice of smoothing parameter (σ) plays a major role in the prediction ability of the PNN which is addressed using a novel technique namely, WCGWO. The WCGWO algorithm is used to select the optimal value of σ in PNN.Results: These algorithms have been successfully implemented using the Hadoop framework. The proposed model is tested by using three large and one small microarray datasets, and a comparative analysis is carried out with the existing FS and classification techniques. The results suggest that WCGWO-mrPNN can outperform other methods for high dimensional microarray classification.Conclusion: The effectiveness of the proposed methods are compared with other existing schemes. Experimental results reveal that the proposed scheme is accurate and robust. Hence, the suggested scheme is considered to be a reliable framework for microarray data analysis.Significance: Such a method promotes the application of parallel programming using Hadoop cluster for the analysis of large-scale genomics data, particularly when the dataset is of high dimension.

Multi-UAV coordination control by chaotic grey wolf optimization based distributed MPC with event-triggered strategy

The paper proposes a new swarm intelligence-based distributed Model Predictive Control (MPC) approach for coordination control of multiple Unmanned Aerial Vehicles (UAVs). First, a distributed MPC framework is designed and each member only shares the information with neighbors. The Chaotic Grey Wolf Optimization (CGWO) method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem (FHOCP). Then, the distributed cost function is designed and integrated into each FHOCP to achieve multi-UAV formation control and trajectory tracking with no-fly zone constraint. Further, an event-triggered strategy is proposed to reduce the computational burden for the distributed MPC approach, which considers the predicted state errors and the convergence of cost function. Simulation results show that the CGWO-based distributed MPC approach is more computationally efficient to achieve multi-UAV coordination control than traditional method.

Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization- based backstepping control with sliding mode extended state observer

In this paper, a new swarm intelligent-based backstepping control scheme is proposed for quadrotor trajectory tracking and obstacle avoidance. First, the sliding mode extended state observer (SMESO) is used to estimate different disturbances, and the tracking differentiator (TD) is integrated to enhance the performance of backstepping control scheme. Then, the chaotic grey wolf optimization (CGWO) is developed with chaotic initialization and chaotic search to optimize the parameters of attitude and position controllers. Further, the virtual target guidance approach is proposed for quadrotor trajectory tracking and obstacle avoidance. Comparative simulations and Monte Carlo tests are carried out to demonstrate the effectiveness and robustness of the CGWO-based backstepping control scheme and virtual target guidance approach.

Application of QOCGWO-RFA for maximum power point tracking (MPPT) and power flow management of solar PV generation system

A hybrid technique for solar PV array (SPV) generating system for maximizing the power to load is proposed in this dissertation. The proposed hybrid technique is the joint execution of both the Quasi Oppositional Chaotic Grey Wolf Optimizer (QOCGWO) with Random Forest Algorithm (RFA) and hence it is named as QOCGWO-RFA technique. Here, QOCGWO optimizes the exact duty cycles required for the DC-DC converter of SPV based on the voltage and current parameters. RFA predicts the control signals of the voltage source inverter (VSI) based on the active and reactive power variations in the load side. With this control technique, the system parameter variations and external disturbances are reduced and the load demands are satisfied optimally. The proposed strategy is implemented in MATLAB/Simulink working platform with three different case studies and compared with existing techniques. With these case studies, the proposed technique generates the optimal PV power of 2.1 kW.

Hybridization of Chaotic Grey Wolf Optimizer and Dragonfly Algorithm for Short-Term Hydrothermal Scheduling

The short-term hydrothermal scheduling (STHS) is a complicated optimization problem, considering valve-point effect (VPE) and transmission losses, it exhibits the characteristics of nonlinearities and non-smoothness which increase the difficulty of solving the problem. Responding to these challenges, a hybrid algorithm that comprises chaotic local search technique, grey wolf optimizer, and dragonfly algorithm (CGWO-DA) is proposed to settle the STHS problem. The grey wolf optimizer (GWO) has strong local search ability with the slow convergence rate. The dragonfly algorithm (DA) owns superior global search capability and fast convergence rate, however, it exerts premature convergence and is easy to fall into local optimum. Using the chaotic local search as a supplement, the hybrid algorithm combined by two excellent algorithms can improve the shortcoming of each algorithm and enhance the exploration and exploitation of the algorithm simultaneously. In addition, two novel constraints handling strategies are presented to resolve the complicated equality constrains and guarantee the feasibility of solutions. Three systems have been tested by the proposed methodology. The solutions are compared with the ones from other available literatures, the feasibility and effectiveness of the proposed approach are well demonstrated, and the findings of case studies show that the proposed method has great competitive advantages to obtain better scheduling schemes.

Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization-based active disturbance rejection control

Abstract In this paper, a new active disturbance rejection control (ADRC) scheme based on swarm intelligent method is proposed for quadrotors to achieve trajectory tracking and obstacle avoidance. First, the finite-time convergent extended state observer (FTCESO) is designed to enhance the performance of ADRC controller. Then, the chaotic grey wolf optimization (CGWO) algorithm is developed with chaotic initialization and chaotic search to obtain the optimal parameters of attitude and position controllers. Further, a novel virtual target guidance approach is proposed to achieve obstacle avoidance for quadrotors. Comparative simulations are presented to demonstrate the effectiveness and robustness of the CGWO-based ADRC scheme and the virtual target guidance approach.

Multi-Modal Medical Image Fusion With Adaptive Weighted Combination of NSST Bands Using Chaotic Grey Wolf Optimization

Recently, medical image fusion has emerged as an impressive technique in merging the medical images of different modalities. Certainly, the fused image assists the physician in disease diagnosis for effective treatment planning. The fusion process combines multi-modal images to incur a single image with excellent quality, retaining the information of original images. This paper proposes a multi-modal medical image fusion through a weighted blending of high-frequency subbands of nonsubsampled shearlet transform (NSST) domain via chaotic grey wolf optimization algorithm. As an initial step, the NSST is applied on source images to decompose into the multi-scale and multi-directional components. The low-frequency bands are fused based on a simple max rule to sustain the energy of an individual. The texture details of input images are preserved by an adaptively weighted combination of high-frequency images using a recent chaotic grey wolf optimization algorithm to minimize the distance between the fused image and source images. The entire process emphasizes on retaining the energy of the low-frequency band and the transferring of texture features from source images to the fused image. Finally, the fused image is formed using inverse NSST of merged low and high-frequency bands. The experiments are carried out on eight different disease datasets obtained from Brain Atlas, which consists of MR-T1 and MR-T2, MR and SPECT, MR and PET, and MR and CT. The effectiveness of the proposed method is validated using more than 100 pairs of images based on the subjective and objective quality assessment. The experimental results confirm that the proposed method performs better in contrast with the current state-of-the-art image fusion techniques in terms of entropy, VIFF, and FMI. Hence, the proposed method will be helpful for disease diagnosis, medical treatment planning, and surgical procedure.

An opposition-based chaotic Grey Wolf Optimizer for global optimisation tasks

ABSTRACTReal-world optimisation problems that are not endowed in mathematical characteristics like differentiability, convexity etc. require non-traditional optimisation approaches that explore the promising regions of the search space stochastically to achieve the optima of the problem. Grey Wolf Optimizer (GWO) is one of the efficient and recently developed approaches in the area of Swarm Intelligence to solve real-world optimisation problems over continuous space. However, in some cases, due to the insufficient diversity, GWO still suffers from the problem of stagnation in local optimums. Therefore, this article presents the novel algorithm OCS-GWO that enhances the performance of original GWO by introducing the opposition-based learning to approximate the closer search candidate solution to the global optima and chaotic local search for the exploitation of the search regions efficiently. In OCS-GWO, a chaotic local search is used for balancing the exploration and exploitation operators that are the underlying features of any stochastic search algorithm. The performance of the proposed algorithm OCS-GWO has been evaluated on a set of 23 standard benchmark test problems and on three engineering application problems – gear train, cantilever beam and speed reducer design problems. The experimental results on test problems and engineering applications confirm the efficiency and reliability of the proposed algorithm over original GWO.

An optimal autonomous microgrid cluster based on distributed generation droop parameter optimization and renewable energy sources using an improved grey wolf optimizer

ABSTRACTMicrogrid (MG) clustering is regarded as an important driver in improving the robustness of MGs. However, little research has been conducted on providing appropriate MG clustering. This article addresses this shortfall. It proposes a novel multi-objective optimization approach for finding optimal clustering of autonomous MGs by focusing on variables such as distributed generation (DG) droop parameters, the location and capacity of DG units, renewable energy sources, capacitors and powerline transmission. Power losses are minimized and voltage stability is improved while virtual cut-set lines with minimum power transmission for clustering MGs are obtained. A novel chaotic grey wolf optimizer (CGWO) algorithm is applied to solve the proposed multi-objective problem. The performance of the approach is evaluated by utilizing a 69-bus MG in several scenarios.

Chaotic grey wolf optimization algorithm for constrained optimization problems

AbstractThe Grey Wolf Optimizer (GWO) algorithm is a novel meta-heuristic, inspired from the social hunting behavior of grey wolves. This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed. Firstly, detailed studies are carried out on thirteen standard constrained benchmark problems with ten different chaotic maps to find out the most efficient one. Then, the chaotic GWO is compared with the traditional GWO and some other popular meta-heuristics viz. Firefly Algorithm, Flower Pollination Algorithm and Particle Swarm Optimization algorithm. The performance of the CGWO algorithm is also validated using five constrained engineering design problems. The results showed that with an appropriate chaotic map, CGWO can clearly outperform standard GWO, with very good performance in comparison with other algorithms and in application to constrained optimization problems.Highlights Chaos has been introduced to the GWO to develop Chaotic GWO for global optimization. Ten chaotic maps have been investigated to tune the key parameter ‘a’, of GWO. Effectiveness of the algorithm is tested on many constrained benchmark functions. Results show CGWO's better performance over other nature-inspired optimization methods. The proposed CGWO is also used for some engineering design applications.

Chaotic metaheuristic algorithms for learning and reproduction of robot motion trajectories

Most of today’s mobile robots operate in controlled environments prone to various unpredictable conditions. Programming or reprogramming of such systems is time-consuming and requires significant efforts by number of experts. One of the solutions to this problem is to enable the robot to learn from human teacher through demonstrations or observations. This paper presents novel approach that integrates Learning from Demonstrations methodology and chaotic bioinspired optimization algorithms for reproduction of desired motion trajectories. Demonstrations of the different trajectories to reproduce are gathered by human teacher while teleoperating the mobile robot in working environment. The learning (optimization) goal is to produce such sequence of mobile robot actuator commands that generate minimal error in the final robot pose. Four different chaotic methods are implemented, namely chaotic Bat Algorithm, chaotic Firefly Algorithm, chaotic Accelerated Particle Swarm Optimization and newly developed chaotic Grey Wolf Optimizer (CGWO). In order to determine the best map for CGWO, this algorithm is tested on ten benchmark problems using ten well-known chaotic maps. Simulations compare aforementioned algorithms in reproduction of two complex motion trajectories with different length and shape. Moreover, these tests include variation of population in swarm and demonstration examples. Real-world experiment on a nonholonomic mobile robot in indoor environment proves the applicability of the proposed approach.