The whale optimization algorithm (WOA) is motivated by the predatory nature of bubble nets and mimics dwindling and encircling, bubble net persecuting, and randomized wandering and foraging actions to locate the expan...
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The whale optimization algorithm (WOA) is motivated by the predatory nature of bubble nets and mimics dwindling and encircling, bubble net persecuting, and randomized wandering and foraging actions to locate the expansive adequate value. However, the WOA has several deficiencies: inadequate resolution accuracy, sluggish convergence speed, susceptibility to search stagnation, and insufficient localized detection efficiency. A quantum encoding WOA (QWOA) is introduced for global optimization and adaptive infinite impulse response (IIR) system identification. The quantum encoding mechanism exploits the principle of a quantum bit to encode a search agent, which manipulates the state of an essential quantum bit and amends the location data. The quantum rotation gate modulates the quantum bit's configuration, the quantum NOT gate accomplishes bit mutation and prohibits precocious convergence. The probability amplitude of the quantum bit represents the multistate superposition state of the search agent, which enriches the population diversity, advances individualized information, broadens the detection scope, inhibits premature convergence, facilitates estimation effectiveness, and promotes solution accuracy. The QWOA not only promptly locates the search scope nearest the most appropriate solution but also computes the spiral-shaped encircling route to promote predation diversification. Twenty-three benchmark functions, eight real-world engineering layouts, and adaptive IIR system identification are utilized to assess the QWOA's feasibility and effectiveness. The experimental results reveal that QWOA successfully equalizes exploration and exploitation to accelerate convergence speed, ameliorate calculation accuracy, and strengthen stability and robustness.
With the high population of electric vehicle adoption, precisely controlling the temperature of the battery modules is essential to provide long-term sustainability and reliability. In the amount of battery thermal ma...
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With the high population of electric vehicle adoption, precisely controlling the temperature of the battery modules is essential to provide long-term sustainability and reliability. In the amount of battery thermal management techniques, adding spoilers is a promising method for enhancing the heat transfer performance of cold plates, but the performance of the system is highly sensitive to different geometry parameters. In this study, heat transfer performance for the cold plates battery thermal management system with a tesla flow channel was numerically and experimentally investigated with a focus on the geometry parameters, including spoiler length (L), mounting position (X), and coolant flow rate (nu). A modified heuristic-based swarm intelligence multiobjective optimizationalgorithm is proposed to obtain the optimal spoiler parameters. The results show the maximum temperature of the battery module is reduced by 3.12 degrees C after adding the spoiler. The optimization spoilers maintain the maximum temperature of the battery module below 30.9 degrees C, and the best spoiler parameters as L = 5.10 mm, X = 14.3 mm, and v = 1.186 x 10-2 m/s. This study uses a heuristic-based swarm intelligence multi-objective optimizationalgorithm to investigate the optimization process of the spoiler structural parameters and provides guidance for the application of advanced multi-objective optimizationalgorithms in cold plate design.
whale optimization algorithm (WOA) is a new bio-meta-heuristic algorithm presented to simulate the predatory humpback whales' behavior in the ocean. In previous studies, WOA has been observed to exhibit lower accu...
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whale optimization algorithm (WOA) is a new bio-meta-heuristic algorithm presented to simulate the predatory humpback whales' behavior in the ocean. In previous studies, WOA has been observed to exhibit lower accuracy and slower convergence rates. In this paper, we propose an improved the WOA by innovatively incorporating an adaptive fitness-distance balance strategy, namely AFWOA. AFWOA can continuously and efficiently identify the maximum potential candidate solutions from the population within the search process, thus improving the accuracy rate and convergence speed of the algorithm. Through various experiments in IEEE CEC2017 and an ill-conditional problem, AFWOA is proven to be more competitive than the original WOA, several other state-of-the-art WOA variants and other four classic meta-heuristic algorithms. (c) 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
This study aims to optimize the velocity change of the Didymos asteroid using the whale optimization algorithm (WOA). The deflection of asteroids that pose significant threats to Earth is a crucial aspect of upcoming ...
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This study aims to optimize the velocity change of the Didymos asteroid using the whale optimization algorithm (WOA). The deflection of asteroids that pose significant threats to Earth is a crucial aspect of upcoming space missions. In this research, a spacecraft is attached to the Didymos asteroid, utilizing its gravitational force as a perturbation to modify the asteroid's trajectory. The transfer of kinetic energy from the spacecraft to the asteroid induces a change in velocity (Delta V). The findings indicate that the most substantial impact on velocity occurs in the radial direction, showing divergent oscillatory behavior. The results suggest that the optimal point for significant velocity change is located shortly after the perihelion. At this point, WOA achieves the maximum velocity change. Additionally, the stability of the asteroid's deflection is investigated due to the nonlinear characteristics of the orbital motion equations. The optimal velocity change is identified as Delta Vtotal = 2.5139 x 10-7 km at Delta t = 27.657(h), occurring after the perihelion at t(h)Delta Vmax . This study introduces a novel optimization approach for asteroid deflection, emphasizing the nonlinear dynamics of orbital motion.
This study introduced a multi-objective optimization framework for vortex pumps, utilizing the whale optimization algorithm (WOA) and Gaussian process regression (GPR) to enhance energy performance under various opera...
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This study introduced a multi-objective optimization framework for vortex pumps, utilizing the whale optimization algorithm (WOA) and Gaussian process regression (GPR) to enhance energy performance under various operating conditions. Initially, 12 design parameters were analysed using the Plackett-Burman test, identifying five critical hydraulic parameters. These parameters formed the basis for optimizing the pump's head and weighted efficiency. A surrogate model database was created using Latin hypercube sampling, and GPR facilitated the optimization process. The application of WOA resulted in a 1.94 m increase in head, a 1.72% rise in efficiency, and a 1.69% improvement in weighted efficiency. Entropy production and rigid vorticity analysis further showed a significant reduction in energy loss across pump components. This research offers a robust framework for the efficient and energy-saving design of vortex pumps.
The whale optimization algorithm (WOA) is a swarm intelligence optimizationalgorithm developed by Mirjalili and Lewis in 2016 based on the foraging behavior of whales. Because of its simplicity and high efficiency, s...
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The whale optimization algorithm (WOA) is a swarm intelligence optimizationalgorithm developed by Mirjalili and Lewis in 2016 based on the foraging behavior of whales. Because of its simplicity and high efficiency, scholars have adopted this algorithm to address various problems in different disciplines. However, standard WOA has the problems of slow convergence speed, insufficient search accuracy, and limited ability to solve complex problems. In order to solve these problems, this paper proposes a multi-strategy hybrid whalealgorithm (MHWOA). Firstly, the calculation speed is accelerated by modifying the parameters;then, the accuracy of the algorithm is improved by incorporating the scatter search strategy;finally, the simulated annealing algorithm is integrated to improve its ability to solve complex problems. The performance differences between MHWOA, the baseline algorithm, and the improved WOA algorithm are compared using the CEC2017 test suite and three real-world engineering problems. In the comparison of processing results of various problems, the calculation accuracy of MHWOA is improved by no less than 1.96%, the calculation error is reduced by no less than 1.83%, and the execution time is improved by no less than 5.6%. In the CNN-MHWOA-based time series electricity load forecasting problem, MHWOA shows the advantages of reduced error and improved fitting degree with the true value compared with the standard WOA.
The development of numerous wireless sensor network (WSN) applications has sparked considerable interest in the use of these networks across various fields. These networks, which do not require infrastructure and are ...
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The development of numerous wireless sensor network (WSN) applications has sparked considerable interest in the use of these networks across various fields. These networks, which do not require infrastructure and are self-organizing, can be rapidly deployed in most locations to collect information about environmental phenomena and transmit it to relevant hubs for appropriate action in emergency situations. Sensor nodes (SNs) in WSNs function as both sensors and relay nodes in relation to one another. As energy in these networks is limited, the nodes are supplied with only a specific amount of power. Because these networks are often located in difficult and remote areas, node batteries cannot be recharged or replaced. As a result, energy conservation is one of the most pressing concerns in these networks. Consequently, this study proposes a novel optimization technique for clustering WSNs, combining the whaleoptimization method and the genetic algorithm. In this work, information is transferred between cluster heads (CHs) and the sink using a combination of whaleoptimization and evolutionary algorithms, focusing on reducing intracluster distances and energy consumption in cluster members (CMs), while achieving near-optimal routing. The implementation results demonstrate that the proposed technique outperforms previous methods in terms of energy consumption, efficiency, delivery rate, and packet transmission latency, considering the evolutionary capabilities of both the whale optimization algorithm and the genetic algorithm.
In this work, a neural network sliding mode control (SMC) scheme is proposed to address the tracking issue of discrete-time multi-agent systems (MASs) with unknown nonlinearities by combining the preview mechanism and...
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In this work, a neural network sliding mode control (SMC) scheme is proposed to address the tracking issue of discrete-time multi-agent systems (MASs) with unknown nonlinearities by combining the preview mechanism and whale optimization algorithm. An augmented error system (AES) was constructed, which includes previewable reference and disturbance signals. A new sliding mode surface is designed for AES, and the stability criteria are proposed for the sliding mode dynamics. Utilizing the preview mechanism and whale optimization algorithm, the neural network-based SMC law is designed to satisfy the discrete-time reachability condition. Two simulation examples are provided to demonstrate that the proposed control scheme can effectively enhance the tracking performance of MASs.
Federated prediction models for energy prosumers create a global model by combining insights from local machine learning models trained on-site without centralizing the data. For time series energy data, this collabor...
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Federated prediction models for energy prosumers create a global model by combining insights from local machine learning models trained on-site without centralizing the data. For time series energy data, this collaborative approach faces challenges due to the non-IID nature of the data, variations in generation patterns, the high number of model parameters, and convergence issues, leading to poor prediction accuracy. This paper introduces a novel federated learning model, FedWOA, which uses the whale optimization algorithm to determine optimal aggregation coefficients based on the local model weight vectors by pondering the updates considering the model performance and data dimensionality construct the global shared model. To handle the non-IID data the prosumers were clustered based on the similarity of their energy profiles using KMeans. FedWOA improves the prediction quality at the prosumer site, with a 16 % average reduction of the mean absolute error compared to FedAVG while demonstrating good convergence and reduced loss.
Permanent magnet synchronous motors (PMSMs) speed control has gained wide application in various fields. Specifically, there is a disadvantage that nonlinear functions in the conventional active disturbance rejection ...
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Permanent magnet synchronous motors (PMSMs) speed control has gained wide application in various fields. Specifically, there is a disadvantage that nonlinear functions in the conventional active disturbance rejection controller (ADRC) is non-differentiable at the piecewise points. Thus, an improved nonlinear active disturbance rejection controller (NLADRC) for permanent magnet synchronous motor speed control via sine function and whale optimization algorithm (WOA), abbreviated as NLADRC-sin-IWOA, is proposed to overcome this drawback. Considering the unsatisfactory control effect caused by the poor active disturbance resisting ability of the traditional PMSM controllers, this paper proposes an improved NLADRC for PMSM, that reconstructs a novel differentiable and smooth nonlinear function, the novel nonlinear function grounded on primitive function by the function of inverse hyperbolic, sine, square functions, and with difference fitting approach;and designs an improved whale optimization algorithm via convergence factor nonlinear decreasing, Gaussian variation and adaptive cross strategies. The experimental results findings show that the improved NLADRC-sin-IWOA has the advantages of response fast, small steady-state error and tiny overshoot. (c) 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
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