In the domain of indoor wireless positioning technology, non-line-of-sight conditions significantly impact signal ranging accuracy, thereby diminishing the positioning accuracy of conventional least squares and CHAN a...
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Constrained multiobjective optimization stands as a focal point in the evolutionary computation community. Some promising algorithms for constrained multiobjective evolutionary optimization have been proposed. However...
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Multi Stage Monte Carlo optimization (MSMCO), also called statistical optimization, is generally used on a complex nonlinear multivariate problem that classical mathematics has difficulty solving. The Fundamental Theo...
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Effective and rapid emergence approaches and responses remain critical in minimizing the impacts of accidents and disasters in the modern urban environment. This article, therefore, draws attention to optimization alg...
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This study introduces a framework for clustering competition coevolution optimization algorithm based on the parallel Lion Swarm optimization Algorithm (LSO). This framework combines clustering and competitive coevolu...
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ISBN:
(纸本)9798400717284
This study introduces a framework for clustering competition coevolution optimization algorithm based on the parallel Lion Swarm optimization Algorithm (LSO). This framework combines clustering and competitive coevolution concepts under existing parallel computing paradigms. Initially, clustering categorizes particles of the total population, followed by parallel computing principles where particles within each classified subpopulation undergo local optimization using distinct optimization mechanisms. After a certain number of iterations, these subpopulations coevolve through an island-based topology. Experimental results demonstrate significant advantages of the proposed algorithm over traditional methods in both CEC2013 benchmark functions and feature selection problems, affirming its potential and effectiveness in practical applications. This framework introduces a novel approach and method for addressing complex problems, offering broad prospects for application. 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Autonomous vehicles for intelligent surveillance in rural areas increasingly demand low-cost and reliable data collection technologies to perform dense monitoring across extended areas. Backscattering communication ha...
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Autonomous vehicles for intelligent surveillance in rural areas increasingly demand low-cost and reliable data collection technologies to perform dense monitoring across extended areas. Backscattering communication has been employed for this purpose, primarily for low-cost and energy efficiency reasons. This paper considers a backscattering data collection system empowered by unmanned aerial vehicles (UAVs) to overcome the challenge of wireless coverage and provide backscattering tags with physical-layer security. Relevant prior works only focused on the secrecy of backscattering communications, while the limited battery of UAVs was overlooked during the underlying vehicle control. This paper aims to jointly optimize the trajectory of multiple UAVs and choice of tags, as well as tags' reflection parameters, to manage data leakage and total energy consumed by UAVs during a round of data collection. Our specific contributions are threefold. (1) We propose a 3D multiUAV backscattering data collection framework and formulate an optimization problem to maximize the ratio of secrecy across all tags to the power consumption of UAVs subject to some practical constraints. (2) We show that our problem is non-convex and partition it into three sub-problems, transform objective functions, and relax certain constraints to obtain approximate convex problems that yield suboptimal solutions. (3) We evaluate the efficacy of our proposed intelligent security protocol for UAV-assisted data collection, compare its performance with some baseline schemes, our protocal achieve leading performance in terms of secrecy energy efficiency. We also provide the impact of parameters on the secrecy energy efficiency, as well as quantify its complexity via extensive simulations.
The use of solar photovoltaics to produce electricity is becoming more widespread. The search for the best optimal solar PV models presents the most challenge, owing to their non-linear current versus voltage characte...
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The maximum power delivered by a photovoltaic system is greatly influenced by atmospheric conditions such as irradiation and temperature and by surrounding objects like trees, raindrops, tall buildings, animal droppin...
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The maximum power delivered by a photovoltaic system is greatly influenced by atmospheric conditions such as irradiation and temperature and by surrounding objects like trees, raindrops, tall buildings, animal droppings, and clouds. The partial shading caused by these surrounding objects and the rapidly changing atmospheric parameters make maximum power point tracking (MPPT) challenging. This paper proposes a hybrid MPPT algorithm that combines the benefits of the salp swarm algorithm (SSA) and hill climbing (HC) techniques. As long as the rate of change of irradiance does not exceed a specific limit, the HC mode is applied to track the global maximum power point (GMPP). Once a high rate of change in irradiation is detected, the SSA mode is activated. Moreover, the proposed algorithm employs the concept of boundary conditions to handle fast and slow fluctuating irradiance patterns. A comprehensive comparative evaluation of the proposed hybrid SSA-HC with state-of-the-art MPPT algorithms has been undertaken. Four distinct cases have been examined, including irradiance conditions with varying rates of change and partial shading conditions. The proposed hybrid SSA-HC algorithm has been validated and tested using a developed hardware setup, simulated in MATLAB for solar photovoltaic (PV) systems, and compared with standard SSA and HC. The performance of the tracking capability of this proposed hybrid technique at both steady-state and dynamic conditions under rapid and gradual irradiance changes demonstrates its superiority over recent state-of-the-art algorithms.
We expose in a tutorial fashion the mechanisms which underlie the synthesis of optimization algorithms based on dynamic integral quadratic constraints. We reveal how these tools from robust control allow to design acc...
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ISBN:
(纸本)9798350301243
We expose in a tutorial fashion the mechanisms which underlie the synthesis of optimization algorithms based on dynamic integral quadratic constraints. We reveal how these tools from robust control allow to design accelerated gradient descent algorithms with optimal guaranteed convergence rates by solving small-sized convex semi-definite programs. It is shown that this extends to the design of extremum controllers, with the goal to regulate the output of a general linear closed-loop system to the minimum of an objective function. Numerical experiments illustrate that we can not only recover gradient decent and the triple momentum variant of Nesterov's accelerated first order algorithm, but also automatically synthesize optimal algorithms even if the gradient information is passed through non-trivial dynamics, such as time-delays.
As hyper-parameters are ubiquitous and can significantly affect the model performance, hyper-parameter optimization is extremely important in machine learning. In this paper, we consider a sub-class of hyper-parameter...
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ISBN:
(纸本)9781713899921
As hyper-parameters are ubiquitous and can significantly affect the model performance, hyper-parameter optimization is extremely important in machine learning. In this paper, we consider a sub-class of hyper-parameter optimization problems, where the hyper-gradients are not available. Such problems frequently appear when the performance metric is non-differentiable or the hyper-parameter is not continuous. However, existing algorithms, like Bayesian optimization and reinforcement learning, often get trapped in local optimals with poor performance. To address the above limitations, we propose to use cubic regularization to accelerate convergence and avoid saddle points. First, we adopt stochastic relaxation, which allows obtaining gradient and Hessian information without hyper-gradients. Then, we exploit the rich curvature information by cubic regularization. Theoretically, we prove that the proposed method can converge to approximate second-order stationary points, and the convergence is also guaranteed when the lower-level problem is inexactly solved. Experiments on synthetic and real-world data demonstrate the effectiveness of our proposed method.
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