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. ...
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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 replenishmen...
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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 whaleoptimizationalgorithm, 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.
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, ...
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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), whaleoptimizationalgorithm (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.
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...
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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 optimizationalgorithm 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.
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 equippe...
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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.
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 ...
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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 modifiedwhale 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
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 ...
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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.
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...
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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.
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...
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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.
This study addresses the critical challenges posed by the capacitated vehicle routing problem (CVRP), particularly in the logistics of cement transportation under capacity constraints. Existing algorithms, including g...
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This study addresses the critical challenges posed by the capacitated vehicle routing problem (CVRP), particularly in the logistics of cement transportation under capacity constraints. Existing algorithms, including grey wolf optimizer (GWO) and whaleoptimizationalgorithm (WOA), exhibit significant limitations such as imbalanced exploration and exploitation, inefficiency in refining solutions, and inadequate adaptability to dynamic routing conditions. These limitations hinder their ability to provide comprehensive solutions that optimize time, cost, and environmental sustainability. To address these critical challenges, this research proposes an enhanced hybrid metaheuristic algorithm, mGWOA, designed to overcome the limitations of existing approaches by combining the GWO's strong exploitation capabilities and the WOA's exploratory strengths. By integrating opposition-based learning (OBL) to expand the search space and mutation techniques to escape local optima, the mGWOA is tailored to provide more flexible, adaptive, and efficient solutions for the complex and dynamic requirements of the CVRP. The mGWOA framework leverages the exploratory advantages of WOA, the exploitative strengths of GWO, and the diversity-promoting features of OBL and mutation to address the complexities of CVRP. Through computational evaluations in various scenarios, including five case studies ranging from small to large, the algorithm demonstrates its superior ability to generate high-quality solutions, especially as the customer base expands. The results underscore the potential of mGWOA as a robust and adaptive approach to solving CVRP, minimizing time and cost, and contributing to sustainable logistics operations. By bridging existing knowledge gaps, this research provides an innovative global optimization framework, offering practical applications for CVRP and other engineering challenges.
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