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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Girirajan, B. Nijaguna, G.S. Pramodhini, R. Adnan, Myasar Mundher Shivakanth, Gandla SR University Department of ECE Telangana Warangal India S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Yelahanka Department of Electronics and Communication Engineering Bengaluru India The Islamic University Najaf Iraq Koneru Laksmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350317558

In recent years, an efficiency of task scheduling is evolved as a major challenge in cloud platforms. Especially, identifying the optimal resources for input tasks is the major challenges faced by the task scheduler. So, in this research, a modified whale optimization algorithm (MWOA) is proposed to improve the behaviour in task scheduling by applying the parameters such as resource allocation and load balancing. Capacity criteria based MWOA algorithm determines the effective Virtual Machine (VM) for execution of tasks in queue. An effectiveness of proposed WOA algorithm is analysed by the utilization of performance measures such as memory storage, execution time, cost and makespan. The attained results from the proposed WOA algorithm are outperformed in various predictable optimization algorithms such as Storm, Spark, Flink and Kafka. The results of proposed WOA algorithms demonstrates the better performances in minimum execution time of 612ms, and memory storage of 309Kb on Kafka platform. © 2024 IEEE.

关键词： Load Balancing modified whale optimization algorithm Optimal resources Resource Allocation Task Scheduling

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Research on Ship Replenishment Path Planning Based on the modified whale optimization algorithm

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BIOMIMETICS 2025年第3期10卷 179-179页

作者： Chen, Qinghua Yao, Gang Yang, Lin Liu, Tangying Sun, Jin Cai, Shuxiang Navy Aviat Univ Yantai 264001 Peoples R China Yantai Univ Sch Electromech & Automot Engn Yantai 264005 Peoples R China

Ship replenishment path planning has always been a critical concern for researchers in the field of security. This study proposes a modified whale optimization algorithm (MWOA) to address single-task ship replenishment path planning problems. To ensure high-quality initial solutions and maintain population diversity, a hybrid approach combining the nearest neighbor search with random search is employed for initial population generation. Additionally, crossover operations and destroy and repair operators are integrated to update the whale's position, significantly enhancing the algorithm's search efficiency and optimization performance. Furthermore, variable neighborhood search is utilized for local optimization to refine the solutions. The proposed MWOA has been tested against several algorithms, including the original whale optimization algorithm, genetic algorithm, ant colony optimization, hybrid particle swarm optimization, and simulated annealing, using traveling salesman problems as benchmarks. Results demonstrate that MWOA outperforms these algorithms in both solution quality and stability. Moreover, when applied to ship replenishment path planning problems of varying scales, MWOA consistently achieves superior performance compared to the other algorithms. The proposed algorithm demonstrates high adaptability in addressing diverse ship replenishment path planning problems, delivering efficient, high-quality, and reliable solutions.

关键词： ship replenishment path planning modified whale optimization algorithm nearest neighbor search cross operation destroy and repair operators variable neighborhood search

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Optimal Placement of FACTS Devices Using modified whale optimization algorithm for Minimization of Transmission Losses

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IEEE ACCESS 2024年 12卷 130816-130831页

作者： Kar, Manoj Kumar Kanungo, Sanjeet Alsaif, Faisal Ustun, Taha Selim Tolani Maritime Inst Dept Elect & Elect Pune 410507 Maharashtra India Tolani Maritime Inst Marine Engn Dept Pune 410507 Maharashtra India King Saud Univ Coll Engn Dept Elect Engn Riyadh 11421 Saudi Arabia AIST FREA Fukushima Renewable Energy Inst Koriyama 9630298 Japan

A novel modified whale optimization algorithm (MWOA) is presented in this study to minimize transmission losses. The proposed method is used to determine the best control variables, such as reactive power generation, transformer tap settings, and reactive power sources. First, the power flow analysis method is used to determine where the flexible AC transmission system (FACTS) devices should be placed. To achieve the intended objectives, the suggested MWOA approach is applied on multiple IEEE standard test bus systems (IEEE-30, -57, and-118) at different active and reactive loading conditions. The shunt compensation was handled by a Static VAR compensator (SVC) while the series compensation was handled by a thyristor-controlled series compensator (TCSC). The results of applying the MWOA approach are shown and contrasted with those of other promising optimization techniques, including the sine cosine algorithm (SCA), whale optimization algorithm (WOA), moth flame optimization (MFO), grey wolf optimization (GWO), and particle swarm optimization (PSO). The proposed method significantly reduces the active power loss, i.e., 11.11 % in IEEE 30, 50.32 % in IEEE 57 and 15.17% in IEEE 118 bus system at base loading. Ultimately, a comprehensive study of the statistical data was conducted to validate the precision and robustness of the suggested methodology.

关键词： optimization Mathematical models Power system stability Reactive power whale optimization algorithms Propagation losses Thyristors Active power loss load flow analysis modified whale optimization algorithm optimal placement SVC TCSC

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Global optimization of optimal Delaunay triangulation with modified whale optimization algorithm

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ENGINEERING WITH COMPUTERS 2024年第4期40卷 2595-2616页

作者： Weng, Yongjia Cao, Juan Chen, Zhonggui Xiamen Univ Sch Math Sci Xiamen 361005 Fujian Peoples R China Xiamen Univ Fujian Prov Key Lab Math Modeling & Sci Comp Xiamen 361005 Fujian Peoples R China Xiamen Univ Sch Informat Xiamen 361005 Fujian Peoples R China

In this paper, we introduce an innovative approach to generate a high-quality mesh with a density function in a given domain. Our method involves solving a variational problem that optimizes the energy function of the optimal Delaunay triangulation (ODT). To achieve this, we have developed a modified whale optimization algorithm (MWOA) based population that is combined with the quasi-Newton method (L-BFGS) to optimize ODT energy on a global level. Our experiments have demonstrated the impressive efficiency of this optimization algorithm in searching for better minima and producing high-quality meshes. Remarkably, the algorithm's powerful global optimization capability makes it insensitive to initialization, which eliminates the need for any special initialization procedures. Furthermore, our proposed algorithm can easily handle complex domains and non-uniform density functions, making it a versatile tool for mesh generation. Overall, our method offers a promising solution for generating practicable meshes with a density function.

关键词： Mesh generation Optimal Delaunay triangulation modified whale optimization algorithm Global optimization

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Implementation of modified whale optimization algorithm in Two-Degree- of-Freedom Fractional Order-Fuzzy-Proportional-Integral-Derivative Controller for Automatic Generation Control in a Multi-Area Interconnected Power System

ELECTRICA

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ELECTRICA 2023年第2期23卷 281-+页

作者： Sitikantha, Debashis Jena, Narendra Kumar Das, Debiprasanna Sahu, Binod Kumar Siksha O Anusandhan Univ Dept Elect & Elect Engn Bhubaneswar Odisha India Siksha O Anusandhan Univ Dept Elect Engg Bhubaneswar Odisha India

Any mismatch of power demand and power generation in an interconnected electrical power system causes deviances in tie-line power and frequencies. To overcome this issue, an automatic generation control system equipped with intelligent controllers is used in the power system. This study presents a maiden write-up on a two-degree-of-freedom fractional order-fuzzy-proportional-integral-derivative (2DOF-FO-FuzzyPID) controller employed in an interconnected system with different nonlinearities. The controller proposed along with conventional controllers has its gains optimally enumerated by the application of modified whale optimization algorithm (MWOA). Besides this, the potency of the MWOA algorithm over WOA algorithm is examined through some popular benchmark functions. The superior transient response yielded by 2DOF-FO-FuzzyPID controller over PID and fractional-order proportional-integral-derivative controllers of the proposed test system is evaluated. Further, the effectiveness of the projected controller is evaluated by taking the system's nonlinearities and abrupt load perturbation, which acknowledges the robustness of the controller. In spite of these attributes, the stability and relative stability of the 2DOF-FO-FuzzyPID controller is determined in the frequency domain.

关键词： AGC 2DOF-FO-FuzzyPID controller FOPID controller Frequency domain analysis modified whale optimization algorithm

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Eagle strategy using uniform mutation and modified whale optimization algorithm for QoS-aware cloud service composition

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APPLIED SOFT COMPUTING 2022年第0期114卷 108053-108053页

作者： Jin, Hong Lv, Shengping Yang, Zhou Liu, Ying South China Agr Univ Coll Engn Guangzhou Peoples R China Univ Glasgow Sch Engn Glasgow Lanark Scotland

Cloud manufacturing (CMfg) has received increasingly attention from both academia and industry. Cloud service composition is a critical technique in CMfg that connects different available manufacturing cloud services (MCSs) to generate a composite manufacturing cloud service (CMCS) to satisfy users' requirements. Many available MCSs with the same or similar functionality but different QoS attributes are deployed in the CMfg platform. So it is challenging to obtain an optimal CMCS to satisfy the users' complex requirements. Considerable numbers of approaches have been proposed to solve this problem. However, most of them often fall in a local optimum instead of the global one. In this paper, a novel eagle strategy using uniform Mutation and modified whale optimization algorithm (MWOA) is proposed to maintain a balance between the global and local search abilities. In this approach, the uniform mutation is applied to perform the global search to preserve the diversification of the population, and a modified whale optimization algorithm is designed to perform the local search. The performance of the new approach is verified on various benchmark functions and different scales of QoS-aware cloud service composition problems. The experimental results demonstrate that the proposed MWOA has superior performance over the other methods. (C) 2021 Published by Elsevier B.V.

关键词： Cloud manufacturing Quality of service (QoS) Service composition Eagle strategy modified whale optimization algorithm

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Optimal parameter estimation of 1-phase and 3-phase transmission line for various bundle conductor's using modified whale optimization algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 138卷 107893-107893页

作者： Shaikh, Muhammad Suhail Hua, Changchun Raj, Saurav Kumar, Shubash Hassan, Mannan Ansari, Muhammad Mohsin Jatoi, Munsif Ali Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China Inst Chem Technol Dept Elect Engn Marathwada Campus Jalna India Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China Zhejiang Univ Dept Elect Engn Hangzhou Zhejiang Peoples R China Salim Habib Univ Dept Biomed Engn Karachi Sindh Pakistan

The overhead power line transmits and distributes electricity across large regions it has several conductors on poles. Power system stability analysis and state estimates need a thorough understanding of transmission line parameters. To compute the overhead AC transmission line parameters, authors have proposed a modified whale optimization algorithm based on levy flight from four perspectives: dimension selection, exploration controls, modified encircling prey, and candidate solution choice. The proposed work suggested modified whale opti-mization algorithms to calculate capacitance and inductance per unit length for single-phase and three-phase for the different number of bundle conductors. At first, the performance of the algorithm is evaluated and compared with different optimization techniques on 23 standard benchmark functions that are used to evaluate their abilities to discover optimum solutions. Then, the proposed technique is applied in determining optimal parameter settings of 1-phase and 3-phase overhead transmission lines while considering different combinations of bundle conductors for inductor and capacitor. From the results, it can be concluded that the MWOA technique provides more accuracy and reliability to obtain global or near-global optimal settings of control variables. Also, results reveal the proposed technique has the potent to solve real-world optimization problems and is compet-itive with recent methods

关键词： modified whale optimization algorithm Single and three-phase Capacitance Inductance Bundle conductor Transmission line Voltage stability

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Alleviation and control of chaotic oscillations in SMIB power systems using a modified-whale optimization-based battery-STATCOM

JOURNAL OF ENGINEERING RESEARCH

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JOURNAL OF ENGINEERING RESEARCH 2024年第2期12卷 135-153页

作者： Singh, Sheetal Saini, Sanju Deenbandhu Chhotu Ram Univ Sci & Technol Elect Engn Dept Murthal India KIET Grp Inst Elect & Elect Engn Dept Ghaziabad India

Background: Chaotic oscillations within the power system give rise to instability. While these oscillations may not have an immediate impact on the synchrony of the machine, they stimulate one of the oscillation modes, ultimately leading to voltage collapse and a loss of synchronism. Objective: This paper introduces a modified whale optimization algorithm (WOA)-based Battery-STATCOM (Static Synchronous Compensator) as a solution to mitigate chaotic oscillations within a Single Machine Infinite Bus (SMIB) system. Methodology: An adaptable controller is implemented to manage the gate signal within the Battery-STATCOM. The AC-DC currents of this controller are optimally governed by two distinct WOA-tuned Proportional-Integral (PI) controllers. The battery storage unit serves as a robust voltage source, with the intelligent controller maintaining the DC-link voltage at the desired level. Test Cases: Additional disturbances, such as gradual variations in reference voltage and electromagnetic torque, are introduced to exacerbate chaotic oscillations. This is done to assess the controller's real-world performance under adverse conditions. Results and Conclusion: Under zero damping conditions, rotor parameters, including rise time, settling time, peak time, and overshoot, initially remain undefined due to uncontrolled oscillations. However, once the BatterySTATCOM is applied, these parameters are defined and achieve values (in seconds) of 0.90, 6.21, 1.71, and 21.10, respectively. After further optimization through the proposed modified WOA optimizer, the parameters reach values of 0.25, 1.01, 0.89, and 1.78, respectively. These results underscore the effectiveness of the proposed metaheuristic controller in suppressing overall chaotic oscillations within the power system.

关键词： modified whale optimization algorithm Chaotic Oscillations Battery-STATCOM Voltage Collapse PI controller

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An enhanced deep learning model for high-speed classification of plant diseases with bioinspired algorithm

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JOURNAL OF SUPERCOMPUTING 2024年第3期80卷 3713-3737页

作者： Ahila, A. Prema, V. Ayyasamy, S. Sivasubramanian, M. Sethu Inst Technol Dept Elect & Commun Engn Virudunagar 626115 Tamil Nadu India JP Coll Arts & Sci Dept Comp Applicat Tenkasi 627852 Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Vellore Campus Vellore 632014 Tamilnadu India JP Coll Arts & Sci Dept Comp Sci Tenkasi 627852 Tamil Nadu India

Agriculture is one of the most crucial aspects of a nation's growth. However, the quality and quantity of crop yield are severely affected by various plant diseases. Plant diseases must be identified and prevented at an early stage to improve food quality and production rate. The emerging deep learning network of convolutional neural networks (CNNs) achieved excellent results in plant disease classification. However, the classification potential of the network depends largely on the configuration of hyperparameters. Finding the optimal set of hyperparameters is a tedious, time-consuming, and challenging task. To tackle such an issue, this paper proposes an optimized CNN integrated with a novel modified whale optimization algorithm (MWOA) to achieve plant disease classification. Here, the novel method of optimizing CNN hyperparameters facilitates quicker implementation. To boost the proposed model's efficiency, several data augmentation methods are used such as rotation, scaling, and flipping. The proposed MWOA-CNN model is implemented using a plant village database that includes fourteen plant species, 38 disease classes, and healthy leaves as well. The experiential findings showed that the proposed model outperforms the existing models by attaining the highest classification accuracy of 99.92%, resulting in an effective model for plant disease classification.

关键词： Deep learning Hyperparameters optimization Image augmentation Plant disease classification modified whale optimization algorithm

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Novel Meta-Heuristic algorithm for Feature Selection, Unconstrained Functions and Engineering Problems

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IEEE ACCESS 2022年 10卷 40536-40555页

作者： El-Kenawy, El-Sayed M. Mirjalili, Seyedali Alassery, Fawaz Zhang, Yu-Dong Eid, Marwa Metwally El-Mashad, Shady Y. Aloyaydi, Bandar Abdullah Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Delta Univ Sci & Technol Fac Articial Intelligence Mansoura 35712 Egypt Delta Higher Inst Engn & Technol DHIET Dept Commun & Elect Mansoura 35111 Egypt Torrens Univ Australia Ctr Articial Intelligence Res & Optimizat Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea Taif Univ Coll Comp & Informat Technol Dept Comp Engn At Taif 21944 Saudi Arabia Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH Leics England Benha Univ Fac Engn Shoubra Dept Comp Syst Engn Banha 13511 Egypt Qassim Univ Mech Engn Dept Buraydah 51452 Saudi Arabia Mansoura Univ Fac Engn Comp Engn & Control Syst Dept Mansoura 35516 Egypt Shaqra Univ Coll Comp & Informat Technol Dept Comp Sci Shaqra 11961 Saudi Arabia Ain Shams Univ Fac Comp & Informat Sci Dept Comp Sci Cairo 11566 Egypt

This paper proposes a Sine Cosine hybrid optimization algorithm with modified whale optimization algorithm (SCMWOA). The goal is to leverage the strengths of WOA and SCA to solve problems with continuous and binary decision variables. The SCMWOA algorithm is first tested on nineteen datasets from the UCI Machine Learning Repository with different numbers of attributes, instances, and classes for feature selection. It is then employed to solve several benchmark functions and classical engineering case studies. The SCMWOA algorithm is applied for solving constrained optimization problems. The two tested examples are the welded beam design and the tension/compression spring design. The results emphasize that the SCMWOA algorithm outperforms several comparative optimization algorithms and provides better accuracy compared to other algorithms. The statistical analysis tests, including one-way analysis of variance (ANOVA) and Wilcoxon's rank-sum, confirm that the SCMWOA algorithm performs better.

关键词： Machine learning algorithms Metaheuristics Mathematical models Feature extraction whales Spirals Linear programming Artificial intelligence machine learning optimization sine cosine algorithm modified whale optimization algorithm

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