while electricity is being transmitted from electricity generation stations to the customers, there are power losses in between the transmission lines, which influences the electricity quality and the performance of t...
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ISBN:
(纸本)9781665468879
while electricity is being transmitted from electricity generation stations to the customers, there are power losses in between the transmission lines, which influences the electricity quality and the performance of the distribution network. Power network reconfiguration techniques have been used to decrease power energy losses in transmission lines and to improve the voltage profile in one-of-a-kind nodes of the electric system. There is quantity of optimization algorithms which have been evolved and proposed as tools to look at the performance of a reconfigured electrical distribution network. The aim of this analysis paper is to conduct associate degree investigation, in the form of a comparative analysis, of the different optimization methods, such as the Artificial Intelligence (AI) algorithms that includes Artificial Neutral Networks (ANN), Fuzzy Logic Algorithm (FLA), Genetics Algorithm (GA) and Particle Swarm optimization (PSO) Algorithm. The hybrid bio-inspired algorithm is also reviewed;which is the combination of the genetics algorithm and Particle Swarm optimization (HGAPSO). Really, most of the electrical distribution networks are reconfigured radially and thus modification of the radial shape of the distribution feeders is accomplished with the aid of converting the open/closed states of the isolator s in order that the load may be transferred from one feeder to another.
Neural architecture has been a research focus in recent years due to its importance in deciding the performance of deep networks. Representative ones include a residual network(Res Net) with skip connections and a den...
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Neural architecture has been a research focus in recent years due to its importance in deciding the performance of deep networks. Representative ones include a residual network(Res Net) with skip connections and a dense network(Dense Net) with dense connections. However, a theoretical guidance for manual architecture design and neural architecture search(NAS) is still lacking. In this paper, we propose a manual architecture design framework, which is inspired by optimization algorithms. It is based on the conjecture that an optimization algorithm with a good convergence rate may imply a neural architecture with good performance. Concretely, we prove under certain conditions that forward propagation in a deep neural network is equivalent to the iterative optimization procedure of the gradient descent algorithm minimizing a cost function. Inspired by this correspondence, we derive neural architectures from fast optimization algorithms,including the heavy ball algorithm and Nesterov's accelerated gradient descent algorithm. Surprisingly, we find that we can deem the Res Net and Dense Net as special cases of the optimization-inspired *** architectures offer not only theoretical guidance, but also good performances in image recognition on multiple datasets, including CIFAR-10, CIFAR-100, and Image Net. Moreover, we show that our method is also useful for NAS by offering a good initial search point or guiding the search space.
Decision making models described as problems of multicriterial optimization are very complicated for investigation, because the property of criteria contradictoriness leads to the notion of the solution as the set of ...
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With the development of information technology, optimisation algorithms have become increasingly important in the field of diet and nutrition, especially in personalised recipe design. The aim of this study is to appl...
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This study aims to use IGA (Improved Genetic Algorithm) to optimize the design and construction of a personalized practical teaching platform for school enterprise integration. This platform aims to meet the personali...
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This paper delves into the investigation of a distributed aggregative optimization problem within a network. In this scenario, each agent possesses its own local cost function, which relies not only on the local state...
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There are many algorithms nowadays available to find out solution for finding shortest path in AI. This paper gives review of comparative study of various algorithms used to calculate the shortest path to reach the go...
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This paper implements a greedy tabu search algorithm to deal with the offline palletizing problem, and proposes a two-phase greedy tabu search algorithm. In the first phase, a greedy stochastic adaptive search algorit...
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This article describes the practical usage of mathematical algorithms in a computer game to create realistic soil behavior in contact with external objects. The study uses a set of algorithms to simulate the behavior ...
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The technique proposed in this paper, the self-adaptive population hybrid Rao algorithm (SAPHR), is intended to handle single- and multi-objective optimization problems. Unlike traditional methods, it does away with t...
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