This study proposes a novel method based on K-means and the Logistic Chaotic JAYA algorithm (LCJAYA) to resolve the ORPD problem for real power loss minimization, voltage deviation minimization and voltage stability e...
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To tackle the shortcomings of the original WOA, including its sluggish convergence rate and tendency to approach local optima, an adaptive whale optimization algorithm (MWOA) combining chaotic maps and dynamic paramet...
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The rise of English as a global language has led to a significant in English language online teaching system. In the context of the integration of industry and education, it is necessary to use advanced technology to ...
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Bayesian optimization (BO) is a sample-efficient optimization algorithm widely employed across various applications. In some challenging BO tasks, input uncertainty arises due to the inevitable randomness in the optim...
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ISBN:
(纸本)9781713899921
Bayesian optimization (BO) is a sample-efficient optimization algorithm widely employed across various applications. In some challenging BO tasks, input uncertainty arises due to the inevitable randomness in the optimization process, such as machining errors, execution noise, or contextual variability. This uncertainty deviates the input from the intended value before evaluation, resulting in significant performance fluctuations in final result. In this paper, we introduce a novel robust Bayesian optimization algorithm, AIRBO, which can effectively identify a robust optimum that performs consistently well under arbitrary input uncertainty. Our method directly models the uncertain inputs of arbitrary distributions by empowering the Gaussian Process with the Maximum Mean Discrepancy (MMD) and further accelerates the posterior inference via Nystrom approximation. Rigorous theoretical regret bound is established under MMD estimation error and extensive experiments on synthetic functions and real problems demonstrate that our approach can handle various input uncertainties and achieve a state-of-the-art performance.
In recent decades, Distributed Generation (DG) has emerged as the most effective solution for Radial Distribution Systems (RDS) to reduce power losses, primarily due to the significant increase in energy consumption, ...
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In recent decades, Distributed Generation (DG) has emerged as the most effective solution for Radial Distribution Systems (RDS) to reduce power losses, primarily due to the significant increase in energy consumption, while also improving the voltage profile. This paper discusses the application of two algorithms: the White Shark Optimizer (WSO) and the Exponential Distribution Optimizer (EDO). These algorithms are designed to determine the optimal distribution of Renewable Energy Resources (RERs) for Photovoltaic (PV) systems and Wind Turbine Generators (WTGs) within a distribution network. The main objectives are to minimize power losses and enhance the voltage profile of the distribution system. Simulations were conducted across various case studies, considering multiple scenarios to assess the impact of PV and WTG systems on power losses and the Voltage Deviation Index (VDI). The effectiveness of the proposed algorithms is demonstrated through a comprehensive performance analysis applied to the IEEE 33-bus system. Results indicate that, in the best scenario involving multi-objective functions, the WSO can reduce power losses and VDI by up to 90.7 % and 98.98 %, respectively, while improving the minimum voltage from 0.9131 to 0.9804 p.u. These findings were compared with other techniques, highlighting the superiority and effectiveness of the proposed algorithms. Overall, the results show that these algorithms effectively determine the ideal sizes and placements for PV and WTG units, leading to a significant reduction in active power loss and an improvement in the minimum bus voltage.
In this article, we present a unified framework for distributed convex optimization using an algorithm called proximal atomic coordination (PAC). PAC is based on the prox-linear approach and we prove that it achieves ...
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In this article, we present a unified framework for distributed convex optimization using an algorithm called proximal atomic coordination (PAC). PAC is based on the prox-linear approach and we prove that it achieves convergence in both objective values and distance to feasibility with rate o(1/tau), where tau is the number of algorithmic iterations. We further prove that linear convergence is achieved when the objective functions are strongly convex and strongly smooth with condition number kappa(f), with the number of iterations on the order of square-root of kappa(f). We demonstrate how various decomposition strategies and coordination graphs relate to the convergence rate of PAC. We then compare this convergence rate with that of a distributed algorithm based on the popular alternating direction method of multipliers (ADMMs) method. We further compare the algorithmic complexities of PAC to ADMM and enumerate the ensuing advantages. Finally, we demonstrate yet another advantage of PAC related to privacy. All theoretical results are validated using a power distribution grid model in the context of the optimal power flow problem.
Wireless sensor networks (WSNs) often trust on batteries for power, which cannot frequently be recharged or replaced easily across several applications. Hence, in this work, a novel approach using, Whale optimization ...
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Every manufacturing industry strives to always provide impeccable goods. Due to machine failures, labor issues, etc., this is practically unachievable in real-world situations during the manufacturing run time. As a r...
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Every manufacturing industry strives to always provide impeccable goods. Due to machine failures, labor issues, etc., this is practically unachievable in real-world situations during the manufacturing run time. As a result, things of subpar quality are produced by the equipment systems. The inferior-quality products are improved at a cost to make them better, and then they are prepared for sale. The nonlinear programming Lagrangian method is used to determine the best solution, which affects the average monthly cost. In the suggested model, the graded mean integration representation method is used to describe defuzzification while trapezoidal and pentagonal fuzzy numbers are used to calculate the optimal cost though there are different types of fuzzy numbers available that are used to test the optimality. The main aim of the paper is to compare the trapezoidal and pentagonal fuzzy numbers to test the optimal total cost. As a result, the trapezoidal fuzzy number gives an accurate result in all cases, while in the pentagonal fuzzy number, there is a slight deviation in the fuzzy case. So when we go with a higher-order fuzzy number, the accuracy of the optimal total cost changes. Finally, a graphic comparison using MATLAB is carried out for the two fuzzy numbers and the best out of them is found.
This paper presents a fresh global optimization algorithm specifcally designed for the sum-of-linear-ratios problem. Initially, auxiliary variables are introduced to reformulate the original problem into an equivalent...
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A pivotal problem in the Internet of Things (IoT) is resource allocation, where the goal is to optimize allocation strategies of IoT resources. In general, resource allocation problems are formulated as constrained op...
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