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.
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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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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The multi-objective evolutionary algorithm designed for optimizing delivery vehicle routes, particularly within the realm of rural agricultural logistics and transportation, adeptly addresses the myriad challenges enc...
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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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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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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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