Hybrid Whale Optimization Algorithm and Grey Wolf Optimizer Algorithm for Optimal Coordination of Direction Overcurrent Relays

PurposeSea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions.Design/methodology/approachCloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance.FindingsThe performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively.Originality/valueTo the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.

Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. Yet, the slow convergence speed issue in Whale Optimization Algorithm and Grey Wolf Optimizer could demote the performance of feature selection and classification accuracy. Therefore, to overcome this issue, a modified WOA (mWOA) and modified GWO (mGWO) for wrapper-based feature selection were proposed in this study. The proposed mWOA and mGWO were given a new inversed control parameter which was expected to enable more search area for the search agents in the early phase of the algorithms and resulted in a faster convergence speed. The objective of this comparative study is to investigate and compare the effectiveness of the inversed control parameter in the proposed methods against the original algorithms in terms of the number of selected features and the classification accuracy. The proposed methods were implemented in MATLAB where 12 datasets with different dimensionality from the UCI repository were used. kNN was chosen as the classifier to evaluate the classification accuracy of the selected features. Based on the experimental results, mGWO did not show significant improvements in feature reduction and maintained similar accuracy as the original GWO. On the contrary, mWOA outperformed the original WOA in terms of the two criteria mentioned even on high-dimensional datasets. Evaluating the execution time of the proposed methods, utilizing different classifiers, and hybridizing proposed methods with other metaheuristic algorithms to solve feature selection problems would be future works worth exploring.

The standard whale algorithm is easy to fall into the local optimum, in order to further improve the performance of the whale optimization algorithm, improve the local search ability and global search ability of the algorithm, a new hybrid whale optimization algorithm is proposed in this paper. Differential evolution algorithm is combined with the standard whale optimization algorithm, and chaos initialization population and chaos disturbance, adaptive convergence operator, adaptive selection and other operations are added to the algorithm. Several test functions have been used to prove its superiority in accuracy and speed.

Power generation is undergoing a major shift with the rise of renewable energy, and DC microgrids are emerging as key players in transforming electricity generation, distribution, and management. Implementing droop control causes inherent voltage fluctuation at the DC bus. This study presents a distributed secondary control technique for a standalone solar photovoltaic (PV) and battery energy storage‐based DC microgrid. The scheme ensures bus voltage recovery, state of charge (SoC) balancing, and power allocation among multiple energy storage units by integrating current and voltage error corrections within the designed control loop. A novel hybrid whale optimization (WO) algorithm and the gray wolf optimization (GWO) algorithm approach are introduced to optimize the parameters of the proposed control scheme, ensuring the proposed control meets its objectives. State‐space modeling of the DC microgrid was formulated, incorporating eigenvalue observation analysis to assess the impact of the optimized control on the system’s stability. A real‐time testing framework designed with MATLAB/Simulink is implemented within the Speedgoat real‐time machine, enabling the validation of the control technique under realistic operating conditions. The findings indicate that by facilitating power exchange and communication between neighboring energy storage units, the proposed control scheme ensures an accurate and well‐balanced power distribution across the system. Furthermore, the implementation of this strategy effectively stabilizes the DC bus voltage, mitigating fluctuations and enhancing overall microgrid performance.

This article presents a novel hybrid algorithm based on the grey-wolf optimizer and whale optimization algorithm, referred here as grey-wolf optimizer–whale optimization algorithm, for the dynamic parametric modelling of a four degree-of-freedom master–slave robot manipulator system. The first part of this work consists of testing the feasibility of the grey-wolf optimizer–whale optimization algorithm by comparing its performance with a grey-wolf optimizer, whale optimization algorithm and particle swarm optimization using 10 benchmark functions. The grey-wolf optimizer–whale optimization algorithm is then used for the model identification of an experimental master–slave robot manipulator system using the autoregressive moving average with exogenous inputs model structure. Obtained results demonstrate that the hybrid algorithm is effective and can be a suitable substitute to solve the parameter identification problem of robot models.

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.

The 0/1 Knapsack problem is one of the most popular real-world optimization problems that arise in searching space and finding the most optimum solution. Theoretically, the optimum solution problem of the 0/1 Knapsack requires suitable technique to explore the search space effectively. Practically, as many metaheuristic algorithms, Whale Optimization Algorithm (WOA) may fail in local optimum solution. This paper proposes Opposition-based Whale Optimization Algorithm (OWOA) to optimize solution problem in 0/1 Knapsack. The OWOA has been tested original WOA by using twenty cases of Knapsack problem and against other metaheuristic algorithms such as (CGMA) and HS-Jaya. The experimental results indicate a significant performance of the optimization solution and stabilization with minimal standard deviation value. This shows that the OWOA improved the original version WOA and has promising result in comparison with other existing algorithms.

SummaryThe search for an effective nature‐inspired optimization technique has certainly continued for decades. This work proposes a novel robust multi‐user detection algorithm based on Grey wolf optimization and differential evolution algorithm to overcome the problem of high bit error rate (BER) in multi‐user detection under an impulse noise environment. The simulation results show that the iteration times of the multi‐user detector based on the proposed algorithm is less than those of genetic algorithm, differential evolution algorithm, Grey wolf optimization algorithm, salp swarm algorithm, grasshopper optimisation algorithm, and whale optimization algorithm with the lowerst BER value.

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

A new design approach is presented in this study for tuning of proportional-integral-derivative (PID) controller parameters in a DC–DC buck converter utilizing a hybrid whale optimization algorithm (WOA) with simulated annealing (SA), namely the WOASAT algorithm, which uses a tournament selection mechanism. The proposed algorithm’s efficacy ensures that the optimum PID controller parameters are tuned quickly and that the quality of tuning is high. A time domain performance index is utilized to validate the proposed WOASAT-based PID controller’s performance. In addition, from the comparative results of statistical analysis, frequency response analysis, transient response analysis, disturbance rejection analysis, and performance indices analysis, the proposed WOASAT-PID controller was found to be more efficient than the SA-PID controller and WOA-PID controller in enhancing the buck converter’s transient response. Cite this article as: Hekimoğlu B, Ekinci S. Optimally Designed PID Controller for a DC-DC Buck Converter via a Hybrid Whale Optimization Algorithm with Simulated Annealing. Electrica, 2020; 20(1): 19-27.

In this paper, a new hybrid whale optimization algorithm (WOA) called WOA-DE is proposed to better balance the exploitation and exploration phases of optimization. Differential evolution (DE) is adopted as a local search strategy with the purpose of enhancing exploitation capability. The WOA-DE algorithm is then utilized to solve the problem of multilevel color image segmentation that can be considered as a challenging optimization task. Kapur’s entropy is used to obtain an efficient image segmentation method. In order to evaluate the performance of proposed algorithm, different images are selected for experiments, including natural images, satellite images and magnetic resonance (MR) images. The experimental results are compared with state-of-the-art meta-heuristic algorithms as well as conventional approaches. Several performance measures have been used such as average fitness values, standard deviation (STD), peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), Wilcoxon’s rank sum test, and Friedman test. The experimental results indicate that the WOA-DE algorithm is superior to the other meta-heuristic algorithms. In addition, to show the effectiveness of the proposed technique, the Otsu method is used for comparison.

Task scheduling is considered as one of the most critical problems in cloud computing environment. The main target of task scheduling includes scheduling jobs on virtual machines as well as improves performance. This study employed Grey Wolf Optimization (GWO) algorithm with modifications on the fitness function by making it handles multi-objectives insingle fitness; the makespan and cost are the objectives included in the fitness in order to solve task scheduling problem. The main target of this technique is to reduce both cost and makespan. CloudSim tool is used to evaluate the objectives of the proposed method. The simulation results showed that the proposed method (Modified Grey Wolf Optimizer - MGWO) has better performance than both the traditional Grey Wolf Optimization Algorithm (GWO) and Whale Optimization Algorithm (WOA) with makespan based fitness in terms of makespan, cost and degree of imbalance.

To address the challenges of accurately modeling the motion of deep-sea aquaculture vessels under the combined effects of dynamic disturbances from the external environment and coupled disturbances from the liquid levels within the aquaculture tanks, as well as the performance limitations of traditional Active Disturbance Rejection Control (ADRC), this study proposes an improved Active Disturbance Rejection Control strategy based on the ADRC structure, and applies it to the design of the controller for the dynamic positioning system of aquaculture vessels. Furthermore, to tackle the issues of numerous uncertain parameters and tuning difficulties inherent in ADRC, an Improved Whale Optimization Algorithm (IWOA) is introduced for parameter tuning. The optimization algorithm incorporates a chaotic optimization strategy and an improved adaptive inertia weight ω with nonlinear characteristics, which simplifies the complex process of constructing an adaptive function in the Grey Wolf Optimization (GWO) algorithm and addresses the slow global search speed and poor local optimization ability of the traditional Whale Optimization Algorithm (WOA). The final Simulink simulation results demonstrate that the ADRC controller designed with IWOA outperforms traditional WOA, GWO, and Genetic Algorithm-optimized ADRC controllers in terms of control effectiveness and disturbance rejection capability, improved the dynamic positioning control performance of aquaculture vessels.

The effective coordination of directional overcurrent relays (DOCRs) is essential for maintaining the stability and dependability of power systems. This work presents a modified grey wolf optimization (MGWO) approach for addressing the DOCR coordination problem. The MGWO algorithm improves the original grey wolf optimization (GWO) by increasing convergence characteristics and balancing the exploration and exploitation stages. This equilibrium is attained by a dimension learning-hunting method and a quadratic reduction in the control parameter during the optimization phase. DOCR coordination is optimized using the MGWO method, using decision factors such as pickup current, time dial setting, and curve type. The goal is to reduce the total working time of primary relays while maintaining selectivity and shortening the discrimination time between primary and backup relays. The suggested MGWO technique is evaluated on the IEEE 8 bus system with two scenarios and compared to other optimization approaches. The results reveal that MGWO outperforms previous algorithms, achieving improvements in the objective function ranging from 5.52% to 58.19%. Additionally, the DOCR settings created by MGWO are evaluated using ETAP software to assure compliance with operational requirements and prevent violations of DOCR coordination.Keywords: Directional Overcurrent Relay Coordination, Grey Wolf Optimization, Multiple Curves, Pickup Current, Time Dial Setting.