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This paper aims to represent an improved whale optimization algorithm (WOA) based on a Lévy flight trajectory and called the LWOA algorithm to solve engineering optimization problems. The LWOA makes the WOA faster, more robust and significantly enhances the WOA. In the LWOA, the Lévy flight trajectory enhances the capability of jumping out of the local optima and is helpful for smoothly balancing exploration and exploitation of the WOA. It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions.

In this paper, an improved WOA based on a Lévy flight trajectory and called the LWOA algorithm is represented to solve engineering optimization problems.

It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions.

An improved WOA based on a Lévy flight trajectory and called the LWOA algorithm is first proposed.

The purpose of this paper is to enhance the performance of spammer identification problem in online social networks. Hyperparameter tuning has been performed by researchers in the past to enhance the performance of classifiers. The AdaBoost algorithm belongs to a class of ensemble classifiers and is widely applied in binary classification problems. A single algorithm may not yield accurate results. However, an ensemble of classifiers built from multiple models has been successfully applied to solve many classification tasks. The search space to find an optimal set of parametric values is vast and so enumerating all possible combinations is not feasible. Hence, a hybrid modified whale optimization algorithm for spam profile detection (MWOA-SPD) model is proposed to find optimal values for these parameters.

In this work, the hyperparameters of AdaBoost are fine-tuned to find its application to identify spammers in social networks. AdaBoost algorithm linearly combines several weak classifiers to produce a stronger one. The proposed MWOA-SPD model hybridizes the whale optimization algorithm and salp swarm algorithm.

The technique is applied to a manually constructed Twitter data set. It is compared with the existing optimization and hyperparameter tuning methods. The results indicate that the proposed method outperforms the existing techniques in terms of accuracy and computational efficiency.

The proposed method reduces the server load by excluding complex features retaining only the lightweight features. It aids in identifying the spammers at an earlier stage thereby offering users a propitious environment.

Power quality issues highly affect the secure and economic operations of the power system. Although numerous methodologies are reported in the literature, flexible alternating current transmission system (FACTS) devices play a primary role. However, the FACTS devices require optimal location and sizing to perform the power quality enhancement effectively and in a cost efficient manner. This paper aims to attain the maximum power quality improvements in IEEE 30 and IEEE 57 test bus systems.

This paper contributes the adaptive whale optimization algorithm (AWOA) algorithm to solve the power quality issues under deregulated sector, which enhances available transfer capability, maintains voltage stability, minimizes loss and mitigates congestions.

Through the performance analysis, the convergence of the final fitness of AWOA algorithm is 5 per cent better than artificial bee colony (ABC), 3.79 per cent better than genetic algorithm (GA), 2,081 per cent better than particle swarm optimization (PSO) and fire fly (FF) and 2.56 per cent better than whale optimization algorithm (WOA) algorithms at 400 per cent load condition for IEEE 30 test bus system, and the fitness convergence of AWOA algorithm for IEEE 57 test bus system is 4.44, 4.86, 5.49, 7.52 and 9.66 per cent better than FF, ABC, WOA, PSO and GA, respectively.

This paper presents a technique for minimizing the power quality problems using AWOA algorithm. This is the first work to use WOA-based optimization for the power quality improvements.

The purpose of this paper is to predict or even control the food safety risks during the distribution of perishable foods. Considering the food safety risks, the distribution route of perishable foods is reasonably arranged to further improve the efficiency of cold chain distribution and reduce distribution costs.

This paper uses the microbial growth model to identify a food safety risk coefficient to describe the characteristics of food safety risks that increase over time. On this basis, with the goal of minimizing distribution costs, the authors establish a vehicle routing problem with a food safety Risk coefficient and a Time Window (VRPRTW) for perishable foods. Then, the Weight-Parameter Whale Optimization Algorithm (WPWOA) which introduces inertia weight and dynamic parameter into the native whale optimization algorithm is designed for solving this model. Moreover, benchmark functions and numerical simulation are used to test the performance of the WPWOA.

Based on numerical simulation, the authors obtained the distribution path of perishable foods under the restriction of food safety risks. Moreover, the WPWOA can significantly outperform other algorithms on most of the benchmark functions, and it is faster and more robust than the native WOA and avoids premature convergence.

This study indicates that the established model and the algorithm are effective to control the risk of perishable food in distribution process. Besides, it extends the existing literature and can provide a theoretical basis and practical guidance for the vehicle routing problem of perishable foods.

This paper aims to resolve the sizing issues of analog circuit design by using proposed metaheuristic optimization algorithm.

The hybridization of whale optimization algorithm and modified gray wolf optimization (WOA-mGWO) algorithm is proposed, and the same is applied for the automated design of analog circuits.

The proposed hybrid WOA-mGWO algorithm demonstrates better performance in terms of convergence rates and average fitness of the function after testing it with 23 classical benchmark functions. Moreover, a rigorous performance evaluation is done with 20 independent runs using Wilcoxon rank-sum test.

For evaluating the performance of the proposed algorithm, a conventional two-stage operational amplifier is considered. The aspect ratios calculated by simulating the algorithm in MATLAB are later used to design the operational amplifier in Cadence environment using 180nm CMOS standard process.

The hybrid WOA-mGWO algorithm is tailored to improve the exploration ability of the algorithm by combining the abilities of two metaheristic algorithms, i.e. whale optimization algorithm and modified gray wolf optimization algorithm. To build further credence and to prove its profound existence in the latest state of the art, a statistical study is also conducted over 20 independent runs, for the robustness of the proposed algorithm, resulting in best, mean and worst solutions for analog IC sizing problem. A comparison of the best solution with other significant sizing tools proving the efficiency of hybrid WOA-mGWO algorithm is also provided. Montecarlo simulation and corner analysis are also performed to validate the endurance of the design.

By means of the massive environmental and financial reimbursements, wind turbine (WT) has turned out to be a satisfactory substitute for the production of electricity by nuclear or fossil power plants. Numerous research studies are nowadays concerning the scheme to develop the performance of the WT into a doubly fed induction generator-low voltage ride-through (DFIG-LVRT) system, with utmost gain and flexibility. To overcome the nonlinear characteristics of WT, a photovoltaic (PV) array is included along with the WT to enhance the system’s performance.

This paper intends to simulate the control system (CS) for the DFIG-LVRT system with PV array operated by the MPPT algorithm and the WT that plays a major role in the simulation of controllers to rectify the error signals. This paper implements a novel method called self-adaptive whale with fuzzified error (SWFE) design to simulate the optimized CS. In addition, it distinguishes the SWFE-based LVRT system with standard LVRT system and the system with minimum and maximum constant gain.

Through the performance analysis, the value of gain with respect to the number of iterations, it was noted that at 20th iteration, the implemented method was 45.23% better than genetic algorithm (GA), 50% better than particle swarm optimization (PSO), 2.3% better than ant bee colony (ABC) and 28.5% better than gray wolf optimization (GWO) techniques. The investigational analysis has authenticated that the implemented SWFE-dependent CS was effectual for DFIG-LVRT, when distinguished with the aforementioned techniques.

This paper presents a technique for simulating the CS for DFIG-LVRT system using the SWFE algorithm. This is the first work that utilizes SWFE-based optimization for simulating the CS for the DFIG-LVRT system with PV array and WT.

This paper reports a new technique for achieving optimized design for power system stabilizers. In any large scale interconnected systems, disturbances of small magnitudes are very common and low frequency oscillations pose a major problem. Hence small signal stability analysis is very important for analyzing system stability and performance. Power System Stabilizers (PSS) are used in these large interconnected systems for damping out low-frequency oscillations by providing auxiliary control signals to the generator excitation input. In this paper, collective decision optimization (CDO) algorithm, a meta-heuristic approach based on the decision making approach of human beings, has been applied for the optimal design of PSS. PSS parameters are tuned for the objective function, involving eigenvalues and damping ratios of the lightly damped electromechanical modes over a wide range of operating conditions. Also, optimal locations for PSS placement have been derived. Comparative study of the results obtained using CDO with those of grey wolf optimizer (GWO), differential Evolution (DE), Whale Optimization Algorithm (WOA) and crow search algorithm (CSA) methods, established the robustness of the algorithm in designing PSS under different operating conditions.

The purpose of this study is to provide a novel portfolio asset prediction by means of the modified deep learning and hybrid meta-heuristic concept. In the past few years, portfolio optimization has appeared as a demanding and fascinating multi-objective problem, in the area of computational finance. Yet, it is accepting the growing attention of fund management companies, researchers and individual investors. The primary issues in portfolio selection are the choice of a subset of assets and its related optimal weights of every chosen asset. The composition of every asset is chosen in a manner such that the total profit or return of the portfolio is improved thereby reducing the risk at the same time.

This paper provides a novel portfolio asset prediction using the modified deep learning concept. For implementing this framework, a set of data involving the portfolio details of different companies for certain duration is selected. The proposed model involves two main phases. One is to predict the future state or profit of every company, and the other is to select the company which is giving maximum profit in the future. In the first phase, a deep learning model called recurrent neural network (RNN) is used for predicting the future condition of the entire companies taken in the data set and thus creates the data library. Once the forecasting of the data is done, the selection of companies for the portfolio is done using a hybrid optimization algorithm by integrating Jaya algorithm (JA) and spotted hyena optimization (SHO) termed as Jaya-based spotted hyena optimization (J-SHO). This optimization model tries to get the optimal solution including which company has to be selected, and optimized RNN helps to predict the future return while using those companies. The main objective model of the J-SHO-based RNN is to maximize the prediction accuracy and J-SHO-based portfolio asset selection is to maximize the profit. Extensive experiments on the benchmark datasets from real-world stock markets with diverse assets in various time periods shows that the developed model outperforms other state-of-the-art strategies proving its efficiency in portfolio optimization.

From the analysis, the profit analysis of proposed J-SHO for predicting after 7 days in next month was 46.15% better than particle swarm optimization (PSO), 18.75% better than grey wolf optimization (GWO), 35.71% better than whale optimization algorithm (WOA), 5.56% superior to JA and 35.71% superior to SHO. Therefore, it can be certified that the proposed J-SHO was effective in providing intelligent portfolio asset selection and prediction when compared with the conventional methods.

This paper presents a technique for providing a novel portfolio asset prediction using J-SHO algorithm. This is the first work uses J-SHO-based optimization for providing a novel portfolio asset prediction using the modified deep learning concept.

Nowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human–computer interaction as well as speech processing. Generally, it focuses on utilizing the models of machine learning for predicting the exact emotional status from speech. The advanced SER applications go successful in affective computing and human–computer interaction, which is making as the main component of computer system's next generation. This is because the natural human machine interface could grant the automatic service provisions, which need a better appreciation of user's emotional states.

This paper implements a new SER model that incorporates both gender and emotion recognition. Certain features are extracted and subjected for classification of emotions. For this, this paper uses deep belief network DBN model.

Through the performance analysis, it is observed that the developed method attains high accuracy rate (for best case) when compared to other methods, and it is 1.02% superior to whale optimization algorithm (WOA), 0.32% better from firefly (FF), 23.45% superior to particle swarm optimization (PSO) and 23.41% superior to genetic algorithm (GA). In case of worst scenario, the mean update of particle swarm and whale optimization (MUPW) in terms of accuracy is 15.63, 15.98, 16.06% and 16.03% superior to WOA, FF, PSO and GA, respectively. Under the mean case, the performance of MUPW is high, and it is 16.67, 10.38, 22.30 and 22.47% better from existing methods like WOA, FF, PSO, as well as GA, respectively.

This paper presents a new model for SER that aids both gender and emotion recognition. For the classification purpose, DBN is used and the weight of DBN is used and this is the first work uses MUPW algorithm for finding the optimal weight of DBN model.

This paper aims at studying meta-heuristic algorithms. One of the common meta-heuristic optimization algorithms is called grey wolf optimization (GWO). The key aim is to enhance the limitations of the wolves’ searching process of attacking gray wolves.

The development of meta-heuristic algorithms has increased by researchers to use them extensively in the field of business, science and engineering. In this paper, the K-means clustering algorithm is used to enhance the performance of the original GWO; the new algorithm is called K-means clustering gray wolf optimization (KMGWO).

Results illustrate the efficiency of KMGWO against to the GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve CEC2019 benchmark test functions.

Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA and GWO so KMGWO achieved the first rank in terms of performance. In addition, the KMGWO is used to solve a classical engineering problem and it is superior.