The new power load management system, as a modern power monitoring system in the context of rapid development of science and technology, can effectively enhance the communication function and detection performance of ...
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In order to improve the coverage of UAV AD hoc network, reduce the loss of UAV in operation and the number of UAV involved, a network coverage optimization algorithm based on improved moth extinguishing algorithm is p...
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Proximal policy optimization is a reinforcement learning algorithm widely used for training policies. Its goal is to learn how to make decisions through interaction with the environment to maximize some kind of cumula...
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Practical optimization problems may contain differ-ent kinds of difficulties that are often not tractable if one relies on a particular optimization method. Different optimization approaches offer different strengths ...
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The size plays a decisive part in the selection of a heat exchanger. A heat exchanger with a smaller size with equal or higher thermal performance is always desirable. In this paper, the amended differential evolution...
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Multimodal multiobjective optimization problems (MMOPs) present a specific category of multiobjective problems. Unlike conventional multiobjective problems where Pareto opti-mal solutions gather in one or few regions ...
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Modular robots are limited in terms of working duration and performance due to the short-comings of existing motion planning algorithms in energy consumption allocation. This paper proposes an optimization strategy fo...
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In order to make full use of the advantages of artificial bee colony algorithm (ABC) and differential evolution algorithm (DE), a hybrid optimization algorithm based on differential evolution algorithm and artificial ...
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We devise a novel quasi-Newton algorithm for solving unconstrained convex optimization problems. The proposed algorithm is built on our previous framework of the iteratively preconditioned gradient-descent (IPG) algor...
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We devise a novel quasi-Newton algorithm for solving unconstrained convex optimization problems. The proposed algorithm is built on our previous framework of the iteratively preconditioned gradient-descent (IPG) algorithm. IPG utilized Richardson iteration to update a preconditioner matrix that approximates the inverse of the Hessian matrix. In this letter, we substitute the Richardson iteration with a successive over-relaxation (SOR) formulation. The convergence guarantee of the proposed algorithm and its theoretical improvement over vanilla IPG are presented. The algorithm is used in a mobile robot position estimation problem for numerical validation using a moving horizon estimation (MHE) formulation. Compared with IPG, the results demonstrate an improved performance of the proposed algorithm in terms of computational time and the number of iterations needed for convergence, matching our theoretical results.
The purpose of this study is to construct a joint dispatching model of sluice groups by using multi-objective optimization algorithm, and its effectiveness is verified by simulation experiments. Methodologically, firs...
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