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PI Parameters Tuning for Frequency Tracking Control of Wireless Power Transfer System Based on Improved whale optimization algorithm

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IEEE ACCESS 2024年 12卷 13055-13069页

作者： Yang, Xiong Guan, Jiamin State Grid Jiangsu Elect Power Co Ltd Distribut Network Technol Dept Res Inst JSEPRI Nanjing 211103 Peoples R China Nanjing Inst Technol Sch Mech Engn Nanjing 211167 Peoples R China

For the efficiency optimization problem of wireless power transfer system, the most important thing is that the frequency tracking control is needed to make the system work in a resonant state. The parameters of the frequency tracking controller directly determine the control performance. In this paper, we consider using an improved whale optimization algorithm to complete the PI parameter tuning of the frequency tracking controller as a way to save personnel's effort and time on the controller design. This improved whale optimization algorithm mainly has the following improvements: firstly, it uses Kent chaotic mapping to initialize the whale population, which improves the variegation of the initial solution;secondly, it introduces adaptive weight coefficients and nonlinearly improves the convergence factor, which achieves a balance between the algorithm's global and local searching ability;finally, it introduces sine-cosine algorithmic strategy and Cauchy reverse learning strategy, which avoids the problem that the algorithm is easy to mature prematurely. The superiority of the improved algorithm is verified through the testing of eight benchmark functions and the Wilcoxon rank sum test method. Finally, the algorithm is applied to the PI parameter optimization of frequency tracking control, and the phase difference curves under the control of different PI parameters are compared by simulation. The results show that the improved algorithm proposed in this paper has a better performance for the PI parameter tuning of frequency tracking control in wireless power transfer system, which can effectively help reduce the investment of human resources.

关键词： Resonant frequency whale optimization algorithms Tuning Heuristic algorithms Convergence Statistics Social factors Wireless power transfer PI control Energy efficiency frequency tracking control whale optimization algorithm

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Spectrum Sensing in Cognitive Radio Using Actor-Critic Neural Network with Krill Herd-whale optimization algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2019年第1期105卷 335-354页

作者： Bhowmik, Mahua Malathi, P. SPPU PESs Modern Coll Engn Pune Maharashtra India SPPU DY Patil Coll Engn Pune Maharashtra India

Spectrum sensing is the active research area in the Cognitive radio networks that initiates the effective data sharing between the licensed and the unlicensed users of Cognitive Network. Maximizing the detection probability for a provided false alarm rate is a hectic challenge of most of the spectral sensing methods. The paper proposes a spectral sensing method, termed as Krill-Herd whale optimization-based actor critic neural network. The unoccupied spectrum is optimally determined using the proposed method that allocates the free spectrum bands to the primary users instantly such that the delay is minimized due to the effective functioning of the fusion center. For the effective sensing, the Eigen-value-based cooperative sensing is activated in the cognitive radio. The analysis of the proposed method is progressed based on the performance metrics, such as false alarm probability and detection probability. The proposed spectral sensing method outperforms the existing methods that yield a maximum probability of detection and minimum probability of false alarm at a rate of 0.9805 and of 0.009.

关键词： Spectrum sensing Cognitive radio Actor-critic neural network Krill Herd optimization whale optimization algorithm

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An Improved whale optimization algorithm Based on Nonlinear Parameters and Feedback Mechanism

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2022年第1期15卷 1-17页

作者： Sun, Guanglei Shang, Youlin Yuan, Kehong Gao, Huimin Henan Univ Sci & Technol Coll Informat Engn Luoyang 471023 Henan Peoples R China Henan Univ Sci & Technol Sch Math & Stat Luoyang 471023 Henan Peoples R China

whale optimization algorithm, as a relatively novel swarm-based intelligence optimization algorithm, has been extensively utilized in numerous scientific and engineering fields. The intent of this work was to devise a modified WOA based on multi-strategy, named MSWOA, to address somewhat deficiencies of the original WOA, such as converging slowly, stagnating at local minima and poor stability. First, a tent map function is adopted to optimize the distribution of the initial population in problem domain. Second, new iteration-based update strategies of convergence factor and inertia weight are constructed to regulate the balance between global and local search capabilities and improve the optimization ability. Additionally, an optimal feedback strategy is presented in the search for prey stage to enhance the global search ability. Numerical experimental results based on 24 test benchmark functions reveal that the proposed MSWOA significantly improves the standard WOA in terms of solution accuracy and convergence speed, and outperforms the comparison algorithms. Furthermore, the results show that the inertia weight strategy has the greatest effect on the performance of basic WOA performance, followed by the convergence factor, and then the optimal feedback strategy.

关键词： whale optimization algorithm Multi-strategy Tent map function Nonlinear parameters Feedback mechanism

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Hybrid whale optimization algorithm and Grey Wolf Optimizer algorithm for Optimal Coordination of Direction Overcurrent Relays

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2019年第6-7期47卷 644-658页

作者： Korashy, Ahmed Kamel, Salah Jurado, Francisco Youssef, Abdel-Raheem Aswan Univ Fac Engn Dept Elect Engn Aswan Egypt Univ Jaen Dept Elect Engn EPS Linares Jaen Spain Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing Peoples R China South Valley Univ Fac Engn Dept Elect Engn Qena Egypt

In this article, a new hybrid metaheuristic optimization algorithm is proposed to solve the coordination problem of directional overcurrent relays (DOCRs). The proposed algorithm is constructed using hybrid whale optimization algorithm and gray wolf optimizer (HWGO) that enhance the performance and reliability of the traditional whale optimization algorithm (WOA). The proposed method enhances the exploitative phase of the WOA using a leadership hierarchy of the gray wolf optimizer (GWO) to find the best optimum solution. The coordination problem of DOCRs is subject to numerous constraints. The goal function for optimal coordination of DOCRs aims to minimize total operation time for all primary relay without violation in constraints to maintain reliability and security of the electric power system. The effectiveness of the proposed algorithm has been investigated on four different interconnected networks. The results using HWGO algorithm are compared with the original WOA, GWO, and earlier reported results of other optimization techniques. The results prove the viability of the proposed algorithm to solve the DOCR coordination problem and the ability of the proposed algorithm to overcomes the drawbacks and cover the weakness of the conventional WOA.

关键词： optimal coordination whale optimization algorithm gray wolf optimizer direction overcurrent relays coordination time interval

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Diagnosis of each main coronary artery stenosis based on whale optimization algorithm and stacking model

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第5期19卷 4568-4591页

作者： Jin, Ziyu Li, Ning Northeastern Univ Coll Sci Shenyang 110819 Peoples R China

Cardiovascular disease is currently one of the diseases with high morbidity and mortality worldwide. One of the main types is coronary artery disease (CAD), which occurs when one or more of the three main arteries, the left anterior descending (LAD) artery, the left circumflex (LCX) artery, and the right coronary artery (RCA), are narrowed. In this paper, we introduce a computer-aided diagnosis model, which uses the k-nearest neighbor (KNN)-based whale optimization algorithm (WOA) for feature selection and combines stacking model for CAD diagnosis and prediction. In WOA, the values in the solution vectors are all continuous, and a threshold is set for binary-conversion to obtain the optimal feature subsets of each main coronary artery. Then we develop a two-layer stacking model based on the selected feature subsets to diagnosis LAD, LCX and RCA. By the proposed method, we select 17 features for each main artery diagnosis, and the classification accuracy on LAD, LCX, and RCA test sets is 89.68, 88.71 and 85.81%, respectively. On the Z-Alizadeh Sani dataset, we compare the proposed feature selection method with other metaheuristics and compare the performance of WOA based on different wrappers. The experimental results show that, the KNN-based WOA method selects the optimal feature subsets, and the classification performance of the stacking model is better than other machine learning algorithms.

关键词： coronary artery disease whale optimization algorithm feature selection stacking model machine learning

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Lamarckian learning-based whale optimization algorithm to localize non-line-of-sight nodes for achieving reliable data dissemination in vehicular ad-hoc networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第7期34卷 e4738-e4738页

作者： Balamurugan, A. KPR Inst Engn & Technol Dept Comp Sci & Engn Coimbatore 641407 Tamil Nadu India

Non-line-of-sight (NLOS) nodes in vehicular ad-hoc networks (VANETs) are responsible for introducing channel congestion and broadcast storm during data dissemination for sustaining reliable connectivity between vehicular nodes. In this paper, an Integrated Lamarckian Learning and whale optimization algorithm-based NLOS Localization Technique (ILL-WOA-NLOS-LT) is proposed for NLOS localization in order to improve high reliability and low latency during the emergency message sharing. This proposed ILL-WOA-NLOS scheme inherits an optimization process that concentrates on the objective of minimizing latency during warning message delivery. It included the merits of Lamarckian evolution-based learning strategy for strengthening and speeding up the rate of local search. It possesses maximum probability of acquiring higher adaptability through active learning for improving the global convergence speed to attain better localization of NLOS nodes in emergency situations. It also incorporated a better tradeoff between exploitation and exploration for effective NLOS localization with reduced error. The simulation experiments of the proposed ILL-WOA-NLOS scheme conducted through EstiNet 8.1 confirmed its predominance in achieving an excellent mean emergency message dissemination rate of 12.96% mean NLOS node localization rate of 14.21% and the mean neighborhood awareness rate of 13.62% with different number of vehicular nodes, compared to the benchmarked localization approaches considered for investigation. The localization error of the proposed ILL-WOA-NLOS scheme was also identified to be significantly reduced by 12.82% compared to the baseline schemes used for investigation.

关键词： Lamarckian Learning NLOS node localization rate Non‐ Line‐ Of‐ Sight (NLOS) nodes Vehicular Ad‐ hoc Networks (VANETs) whale optimization algorithm

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Prioritized Energy Efficient Task Scheduling algorithm in Cloud Computing Using whale optimization algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期126卷 2231-2247页

作者： Mangalampalli, Sudheer Swain, Sangram Keshari Mangalampalli, Vamsi Krishna Centurion Univ Technol & Management Village Alluri Nagar R Sitapur 761211 Odisha India Chaitanya Inst Sci & Technol Kakinada Andhra Pradesh India

Task Scheduling is one of the important aspects in Cloud Computing. The Primary Objective of the task scheduling is to effectively map tasks on to the corresponding VMs by minimizing makespan and maximizing utilization of resources. Energy consumption is one of the important parameter which needs to be addressed in Cloud Computing. It needs to be minimized as datacenters of Cloud releases huge amount Carbon dioxide which effects the environment. Many of the researchers proposed various scheduling algorithms in Cloud computing to address makespan and resource utilization. This paper proposes a task scheduling algorithm which schedules the tasks on to appropriate VMS based on the calculation of Task and VM priorities and is modeled by using whale optimization algorithm while minimizing energy consumption and power cost at datacenters. Initially we have calculated priorities of Task and VMs to effectively maps tasks onto the VMs and thereby evaluating the multi-objective fitness function which addresses energy consumption and power cost at datacenters. Simulation carried out on CloudSim Simulator and analyzed performance of the proposed algorithm with existing PSO and CS algorithms. Simulation results revealed that proposed algorithm minimizes energy consumption and power cost over the existing algorithms.

关键词： Cloud computing Task scheduling whale optimization algorithm Power cost Energy consumption PSO-Particle swarm optimization CS- Cuckoo search

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Synthesis of Broadside Linear Aperiodic Arrays With Sidelobe Suppression and Null Steering Using whale optimization algorithm

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2018年第2期17卷 347-350页

作者： Zhang, Chengyan Fu, Xiongjun Ligthart, Leo P. Peng, Shuilian Xie, Min Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Delft Univ Technol NL-2628 Delft Netherlands

In this letter, a new optimization algorithm named whale optimization algorithm (WOA) is introduced and applied to the synthesis of uniformly excited broadside linear aperiodic arrays with sidelobe suppression and null steering under constraints on beamwidth requirements. Three design examples of pattern synthesis have been chosen to illustrate the effectiveness and superiority of WOA when compared to synthesis techniques described in the literature. The results show that the WOA achieves lowest maximum sidelobe level combined with deepest null depth level with narrow first null beamwidth relative to other state-of-the-art algorithms. The WOA results and convergence tests demonstrate superior applicability of this novel optimization algorithm.

关键词： Broadside linear aperiodic arrays null steering sidelobe suppression whale optimization algorithm

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Enhanced GRU-based regression analysis via a diverse strategies whale optimization algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Lin, Zesheng FoShan Open Univ Foshan Peoples R China

Taking into account the whale optimization algorithm's tendency to get trapped in local optima easily and its slow convergence rate, this paper proposes a diverse strategies whale optimization algorithm (DSWOA) and uses it to optimize the parameters of GRU, thereby achieving better regression prediction effects. First, an innovative t-distribution perturbation is used to perturb the optimal whale to expand the optimization space of the optimal whale. Secondly, in the random search stage, we perform a Cauchy walk on the whale's position and then use reverse learning to enable the algorithm to effectively navigate away from the local optimum. Finally, we adopt a horizontal learning strategy for all whales and use two random whales to determine the current whale's position. Updated, the results suggest that DSWOA is highly effective in global optimization. By utilizing DSWOA, the parameters of GRU were fine-tuned. The experimental findings reveal that GRU produces promising outcomes on multiple datasets, making it a more effective tool for regression prediction tasks.

关键词： whale optimization algorithm Gated recurrent unit T-distribution perturbation Cauchy walk Reverse learning Horizontal crossover strategy

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A Novel Approach Based on Average Swarm Intelligence to Improve the whale optimization algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第2期47卷 1763-1776页

作者： Dereli, Serkan Sakarya Univ Appl Sci Comp Technol & Programming Dept Sakarya Turkey

In this study, a new technique has been introduced by changing the convergence of the whale optimization algorithm, which has the principle of approaching its prey by following the pack leader strictly. For this, first of all, average position values of the swarm were obtained in each iteration. Later, when the "p" parameter, which is used to add randomness to the progress of the swarm members, is below a certain value, the swarm average was used for each individual to move to the new position. Thus, slow convergence and frequent falling to the local optimum which is considered to be the biggest shortcoming of the algorithm, has been eliminated. The distance of whales from each other and from prey was modeled as a fitness function and the Euclidean distance formula was used for this. A complex engineering problem was chosen to reveal the power of both the classical whale optimization algorithm and the algorithm that includes the proposed new technique. As a result, this new technique introduced has provided a 10 million times improvement in solving this complex engineering problem used in the control of serial robot manipulators.

关键词： whale optimization algorithm Robotics Inverse kinematics Complex engineering problem Heuristic algorithm

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