Robot arm manipulator trajectory tracking using novel sliding mode control based quick power reaching law enhanced by grey wolf optimizer

Phishing could be considered a worldwide problem; undoubtedly, the number of illegal websites has increased quickly. Besides that, phishing is a security attack that has several purposes, such as personal information, credit card numbers, and other information. Phishing websites look like legitimate ones, which makes it difficult to differentiate between them. There are several techniques and methods for phishing detection. The authors present two machine-learning algorithms for phishing detection. Besides that, the algorithms employed are XGBoost and random forest. Also, this study uses particle swarm optimization (PSO) and grey wolf optimizer (GWO), which are considered metaheuristic algorithms. This research used the Mendeley dataset. Precision, recall, and accuracy are used as the evaluation criteria. Experiments are done with all features (111) and with features selected by PSO and GWO. Finally, experiments are done with the most common features selected by both PSO and GWO (PSO ∩ GWO). The result demonstrates that system performance is highly acceptable, with an F-measure of 91.4%.

ABSTRACTGeophysical observables using optimisation algorithms are interpreted in terms of physical properties defining the Earth. The field of optimisation is a dynamic field as no single algorithm can solve all optimisation problems. We implement the grey wolf algorithm, an apex predator‐based method, in optimizing geophysical datasets over a layered earth. The grey wolf optimiser is a swarm‐based meta‐heuristic algorithm and has two extremely interesting social practices, viz., social leadership hierarchy and hunting behaviour. The leadership hierarchy is simulated by employing different types of grey wolves where hunting strategies are implemented as optimisation methods. Global minimum from the grey wolf optimiser has been obtained with a pack of 7 wolves and 1500 iterations. To evaluate the efficacy of the grey wolf optimiser, we performed inversion of noise‐contaminated vertical electrical sounding (apparent resistivity), induced polarisation sounding (apparent chargeability), and magneto‐telluric apparent resistivity data. Subsequently, we implemented grey wolf optimisation on field apparent resistivity, apparent chargeability, and magnetotelluric apparent resistivity data adapted from published literature. Both noise‐contaminated synthetic and field data have been compared with popular population‐based algorithms, i.e., particle swarm optimisation and ant colony optimisation and with a local optimisation algorithm—ridge regression. The sensitivity analysis was performed by inverting the noise‐contaminated datasets using the grey wolf optimiser, particle swarm optimisation and ant colony optimisation with six different search space. It is observed that the grey wolf optimiser is least sensitive to the varied search space. The results obtained from the grey wolf optimiser as compared with other techniques are relatively more stable and the obtained normalised RMS deviation is either less or equal with ridge regression, particle swarm optimisation, or ant colony optimisation. This is due to the fact that the grey wolf optimiser does not converge prematurely and avoids getting trapped in a local minimum, as a balance between exploration and exploitation is maintained. We also compared the grey wolf optimiser using L1‐norm and L2‐norm as misfit functions for field data examples. The grey wolf optimiser using L1‐norm resulted in a more stable solution. The execution time of the grey wolf optimiser is least as compared with other population optimisation techniques. Grey wolf optimisation could be applied for routine interpretation of geophysical datasets.

In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing, requisite nontraditional optimization attitudes to attain the optima of the problem, the present paper puts forth a hybrid multiple-objective approach called hybrid grey wolf and whale optimization (HGWWO) algorithms, that integrates two algorithms, namely, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA), with the purpose of conjoining the advantages of each algorithm for minimizing costs, energy consumption, and total execution time needed for task implementation, beside that improving the use of resources. Assessment of the aims of the proposed approach is carried out with the help of the tool known as CloudSim. As pointed out by the results of the experimental work undertaken, the proposed approach has the capability of performing at a superior level by comparison to the original algorithms GWO and WOA on their own with regard to costs, energy consumption, makespan, use of resources, and degree of imbalance.

Clustering is considered as one of the most primitive technique that aids in prolonging the lifetime expectancy of wireless sensor networks (WSNs). But, the process of cluster head selection concerning energy stabilization for the purposed of prolonging the network life expectancy still remains a major issue in WSNs. In this paper, a hybrid grey wolf and crow search optimization algorithm-based optimal cluster head selection (HGWCSOA-OCHS) scheme was proposed for enhancing the lifetime expectancy of the network by concentrating on the minimization of delay, minimization of distance between nodes and energy stabilization. The grey wolf optimization algorithm is hybridized with the crow search optimization algorithm for resolving the issue of premature convergence that prevents it from exploring the search space in an effective manner. This hybridization of GWO and CSO algorithm in the process of cluster head selection maintains the tradeoff between the exploitation and exploration degree in the search space. The simulation experiments are conducted and the results of the proposed HGWCSOA-OCHS scheme is compared with the benchmarked cluster head selection schemes with firefly optimization (FFO), artificial bee colony optimization (ABCO), grey wolf optimization (GWO), firefly cyclic grey wolf optimisation (FCGWO). The proposed HGWCSOA-OCHS scheme confirmed minimized energy consumption, improved network lifetime expectancy by balancing the percentage of alive and dead sensor nodes in the network.

In this work, we have developed two new intelligent maximum power point tracking (MPPT) techniques for photovoltaic (PV) solar systems. To optimize the PWM duty cycle driving the DC/DC boost converter, we have used two optimization algorithms namely the whale optimization algorithm (WOA) and grey wolf optimization (GWO) so we can tune the PID controller gains. The oscillation around the MPP and the fail accuracy under fast variable isolation are among the well-known drawbacks of conventional MPPT algorithms. To overcome these two drawbacks, we have formulated a new objective fitness function that includes WOA/GWO based accuracy, ripple, and overshoot. To provide the most relevant variable step size, this objective fitness function was optimized using the two aforementioned optimization algorithms (i.e., WOA and GWO). We have carried out several tests on Solarex MSX-150 panel and DC/DC boost converter based PV systems. In the simulation results section, we can clearly see that the two proposed algorithms perform better than the conventional ones in term of power overshoot, ripple and the response time.

The P2P lending industry faces significant challenges in predicting loan defaults due to the high dimensionality of the data, which affects lending decision quality. This study aims to propose the Dynamic ACO + GWO algorithm, designed to enhance the efficiency of feature selection and the accuracy of default predictions. The algorithm combines a dynamic Ant Colony Optimization (ACO) based on Monte Carlo, which effectively regulates the evaporation rate, with the Grey Wolf Optimizer (GWO) to improve population initialization and accelerate convergence. The results show that the Dynamic ACO + GWO algorithm outperforms both ACO + GWO and GWO in several aspects. In terms of execution time, Dynamic ACO + GWO requires only 10.61 s for SA = 50, significantly faster than ACO + GWO at 208.37 s and GWO at 61.68 s. For fitness values, Dynamic ACO + GWO achieves 0.094, which is better than ACO + GWO (0.113) and GWO (0.115). Additionally, the algorithm records a default prediction accuracy of 91.23%, higher than ACO + GWO at 90.58% and GWO at 86.13%. Therefore, the Dynamic ACO + GWO algorithm not only improves execution efficiency but also provides more accurate default predictions, making it a superior solution for feature selection in P2P lending.

This study investigates a new fractional-order nonsingular terminal sliding mode control (FTSMC) leveraging a finite-time extended state observer, a simpler prescribed control, and hybrid grey wolf optimization (GWO) combined with whale optimization algorithm (WOA) for manipulator systems. The new FTSMC system is based on an improved fractional-order terminal sliding surface. Initially, the study experimentally optimizes the dynamic parameters and gains of the controller and the observer with the help of the newly developed GWO-WOA technique. As the next step, the uncertainties including optimization error and external disturbances are estimated by the finite-time extended state observer designed using the sliding mode dynamics. Experimental results of GWO-WOA optimization and joint position tracking for a self-designed articulated manipulator prove the efficacy of the proposed control scheme.

This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the grey wolf optimizer establishes a new standard for further study in metaheuristic clustering algorithms. The performance of the K-means clustering-based grey wolf optimizer is around 34% better than the original grey wolf optimizer algorithm for both numerical test problems and data clustering problems.

In this paper, an optimum planer frame design is achieved using the Grey Wolf Optimizer (GWO) algorithm. The GWO algorithm is a nature involved meta-heuristic which is correlated with grey wolves’ activities in social hierarchy. The objective of the GWO algorithm is to produce minimum weight planer frame considering the material strength requirements specified by American Institute for Steel Construction—Load and Resistance Factor Design (AISC-LRFD). The frame design is produced by choosing the W-shaped cross sections from AISC-LRFD steel sections for a beam and column members. A benchmark problem is investigated in the present work to monitor the success rate in a way of best solution and effectiveness of the GWO algorithm. The result of the GWO algorithm is compared with other meta-heuristics, namely GA, ACO, TLBO and EHS. The results show that the GWO algorithm gives better design solutions compared to other meta-heuristics.

This study investigates the effectiveness of the Firefly Optimizer (FFA), Grey Wolf Optimizer (GWO), and Moth Flame Optimizer (MFO) metaheuristic algorithms in estimating the kinetic parameters of a single-step coal pyrolysis model. By examining the effects of the algorithmic configuration, the initial parameter estimates, and the search space size on the efficacy and efficiency of the optimization run, the research seeks to encourage the qualified engineering application of these algorithms in the field of pyrolysis modeling. Four critical analyses were conducted: convergence efficiency, robustness and repeatability, parameter tuning, and performance on noisy data. MFO and GWO had comparable fitness scores of 1.05×10-4 and 1.04×10-4 respectively in the optimisation run analysis, while FireFly Algorithm (FFA) fell behind with a score of 1.09×10-4. Regarding the calculation time, FFA showed better results than other optimizers with an execution time of 113.75 seconds. MFO showed initial promise in convergence analysis with speedy convergence, whereas GWO progressively enhanced its solutions. Additionally, GWO was shown to be the most dependable algorithm with the lowest values for average fitness score and execution time at 1.07×10-4 and 38.86 seconds. The combined values of standard deviation in fitness value and execution time for GWO were 1.07×10-6 and 0.35 indicating its robustness towards initial parameters. Similar to this, investigations on repeatability emphasized the reliability of the GWO method. Further, the parameter tuning assessments supported the balanced performance of GWO, and the studies of noise handling discovered GWO to be the most robust to noisy data. Overall, GWO is recommended as a one-stop average solution for the general engineered application; however, algorithm choice hinges on the specific requirement.

In the present research, the Grey Wolf Optimizer (GWO) was used to minimize the yearly energy consumption of an office building in Seattle weather conditions. The GWO is a meta-heuristic optimization method, which was inspired by the hunting behavior of grey wolfs. The optimization method was coded and coupled with the EnergyPlus codes to perform the building optimization task. The impact of algorithm settings on the optimization performance of GWO was explored, and it was found that GWO could provide the best performance by using 40 wolfs. The optimized solutions of GWO were compared with other optimization algorithms in the literature, and it was found that the GWO could lead to an excellent optimum solution efficiently. One of the best optimization methods in the literature was Particle Swarm Optimization (PSO), which led to an optimum objective function of 133.5, while GWO resulted in the optimum value of 133. The multi-objective building optimization was also examined by GWO. The results showed that it could provide an excellent archive of non-dominant optimum solutions.

Background: The Particle Swarm Optimization (PSO) algorithm is amongst the utmost favourable optimization algorithms often employed in hybrid procedures by the researchers considering simplicity, smaller count of parameters involved, convergence speed and capability of searching global optima. The PSO algorithm acquires memory and the collaborative swarm interactions enhances the search procedure. The high exploitation ability of PSO which intends to locate the best solution within a limited region of the search domain gives PSO an edge over other optimization algorithms. Whereas, low exploration ability results in lack of assurance of proper sampling of the search domain and thus enhances the chances of rejecting a domain containing high quality solutions. A perfect harmony between exploration and exploitation abilities in the course of selection of best solution is needed. High exploitation capacity makes PSO get trapped in local minima when its initial location is far off from the global minima. OBJECTIVES: The intent of this study is to reform this drawback of PSO of getting trapped in local minima. With an objective to upgrade the potential of Particle Swarm Optimization (PSO) to exploit along with preventing PSO of getting trapped in local minima, we require an algorithm with a positive acceptable exploration capacity. METHODS: We utilized, the recently developed metaheuristic Grey Wolf Optimizer (GWO) emulating the seeking and hunting techniques of Grey wolves for this purpose. In our way, the GWO has been utilized to assist PSO in a manner to unite their strengths and lessen their weaknesses. The proposed hybrid has two driving parameters to adjust and assign the preference to PSO or GWO. RESULTS: To test the act of the proposed hybrid it has been examined in comparison with the PSO and GWO methods. For this, eleven benchmark functions involving different unimodal and multimodal functions have been taken. The PSO, GWO and SGWO pseudo codes were coded in visual basic. In all the functions parameters of PSO and GWO were chosen as: w = 0.7, c1 = c2 = 2, population size = 30, number of iterations = 30. Experiments were redone 25 times for each of the method and for each benchmark function. The methods were compared with regard to their best and worst values besides their average values and standard deviations. The obtained results revealed that in terms of average values and standard deviations our hybrid SGWO outperformed both PSO and GWO notably. CONCLUSION: The outcomes of the experiments reveals that the proposed hybrid is better in comparison to both PSO and GWO in the search ability. Though the SGWO algorithm refines result quality, the computational complexity also gets elevated. Thus, lowering the computational complexity would be another issue of future work. Moreover, we will apply the proposed hybrid in the field of water quality estimation and prediction.

Efficient boosted grey wolf optimizers for global search and kernel extreme learning machine training

As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has a strong ability to get rid of the local optimum due to its integration of chaotic maps. To enhance GWO, CLS is always incorporated into GWO to increase its population diversity and accelerate algorithm's convergence. However, it is still unclear that how may chaotic maps should be used in CLS and how to embed them into GWO. To address these challenging issues, this paper studies both single and multiple chaotic maps incorporated GWOs. Extensive comparative experiments are conducted based on IEEE Congress on Evolutionary Computation (CEC) benchmark test suit. The results show that CLS incorporated GWOs generally perform better than the original GWO, suggesting the effectiveness of such hybridization. Moreover, a remarkable finding of this work is that the piecewise linear chaotic map (PWLCM) and Gaussian map have the most potential to improve the search performance of GWO. Additionally, CLS incorporated GWOs also perform significantly better than some other state-of-the-art meta-heuristic algorithms. This study not only gives more insights into the mechanism of how CLS makes influence on GWO, but also finds that the most suitable choice of chaotic map for it.

Grey Wolf Optimizer (GWO) simulates the grey wolves’ nature in leadership and hunting manners. GWO showed a good performance in the literature as a meta-heuristic algorithm for feature selection problems, however, it shows low precision and slow convergence. This paper proposes a Modified Binary GWO (MbGWO) based on Stochastic Fractal Search (SFS) to identify the main features by achieving the exploration and exploitation balance. First, the modified GWO is developed by applying an exponential form for the number of iterations of the original GWO to increase the search space accordingly exploitation and the crossover/mutation operations to increase the diversity of the population to enhance exploitation capability. Then, the diffusion procedure of SFS is applied for the best solution of the modified GWO by using the Gaussian distribution method for random walk in a growth process. The continuous values of the proposed algorithm are then converted into binary values so that it can be used for the problem of feature selection. To ensure the stability and robustness of the proposed MbGWO-SFS algorithm, nineteen datasets from the UCI machine learning repository are tested. The K-Nearest Neighbor (KNN) is used for classification tasks to measure the quality of the selected subset of features. The results, compared to binary versions of the-state-of-the-art optimization techniques such as the original GWO, SFS, Particle Swarm Optimization (PSO), hybrid of PSO and GWO, Satin Bowerbird Optimizer (SBO), Whale Optimization Algorithm (WOA), Multiverse Optimization (MVO), Firefly Algorithm (FA), and Genetic Algorithm (GA), show the superiority of the proposed algorithm. The statistical analysis by Wilcoxon’s rank-sum test is done at the 0.05 significance level to verify that the proposed algorithm can work significantly better than its competitors in a statistical way.