In this article, multivariable derivative-free optimization algorithms for unconstrained optimization problems are developed. A novel procedure for approximating the gradient of multivariable objective functions based...
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In this article, multivariable derivative-free optimization algorithms for unconstrained optimization problems are developed. A novel procedure for approximating the gradient of multivariable objective functions based on noncommutative maps is introduced. The procedure is based on the construction of an exploration sequence to specify where the objective function is evaluated and the definition of so-called gradient generating functions which are composed with the objective function, such that the procedure mimics a gradient descent algorithm. Various theoretical properties of the proposed class of algorithms are investigated and numerical examples are presented.
This study used a radial function neural network (RBFNN) to create a novel system for calculating high-performance concrete's (HPC) compressive strength (CS) modified with fly ash and blast furnace slag. These adm...
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This study used a radial function neural network (RBFNN) to create a novel system for calculating high-performance concrete's (HPC) compressive strength (CS) modified with fly ash and blast furnace slag. These admixtures could affect the mechanical and physical properties of HPC, and determining it definitely requires experimental efforts and costs. Herein, alternative methods such as machine learning algorithms named RBFNN could be useful to address these questions. The SSA (Salp swarm algorithm) and the artificial hummingbird algorithm (AHA) were utilized in this work to find optimal values of hyperparameters of the RBFNN approach that can be tuned. The suggested models were assessed utilizing a comprehensive dataset including 1030 data rows. Finally, the findings were compared to those documented in the literature. The findings of the calculations, which took into account evaluation metrics, depict that both hybrid SSA-RBFNN and AHA-RBFNN analysis might astonishingly perform good productivity during estimating, with R2 values of 0. 8955 and 0.8608 for SSA-RBFNN and 0.8987 and 0.8643 for AHA-RBFNN, respectively, related to the test and train segments. In conclusion, the AHA-RBFNN model created for predicting the CS of HPC amended with BFS and FA could be identified as the proposed model to be applied in practical applications.
There have always been problems such as difficulty in electricity payment and electricity consumption in remote areas, and at present, the payment mode of satellite communication has become extremely convenient and ef...
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The abundance of data has led to the emergence of a variety of optimization techniques that attempt to leverage available side information to provide more anticipative decisions. The wide range of methods and contexts...
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The abundance of data has led to the emergence of a variety of optimization techniques that attempt to leverage available side information to provide more anticipative decisions. The wide range of methods and contexts of application have motivated the design of a universal unitless measure of performance known as the coefficient of prescriptiveness. This coefficient was designed to quantify both the quality of contextual decisions compared to a reference one and the prescriptive power of side information. To identify policies that maximize the former in a data-driven context, this paper introduces a distributionally robust contextual optimization model where the coefficient of prescriptiveness substitutes for the classical empirical risk minimization objective. We present a bisection algorithm to solve this model, which relies on solving a series of linear programs when the distributional ambiguity set has an appropriate nested form and polyhedral structure. Studying a contextual shortest path problem, we evaluate the robustness of the resulting policies against alternative methods when the out-of-sample dataset is subject to varying amounts of distribution shift. Copyright 2024 by the author(s)
The Whale optimization Algorithm (WOA), inspired by humpback whales' hunting behavior, has emerged as a promising metaheuristic optimization technique. This literature review delves into the algorithm's core p...
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A multi-constraint mathematical model for the optimization problem is established in addressing the deck barge fleet stowage optimization problem for large-scale cargoes. A novel encoding scheme is proposed to enable ...
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Grey Wolf Optimizer (GWO) is a leading swarm intelligence optimization algorithm that restores the predation behavior of gray wolves and uses their collective cooperation to achieve algorithm optimization. However, th...
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With the development of economy and society, the layout of China's Internet of Vehicles industry has been continuously improved. RSUs play an important role in the Internet of Vehicles. But it faces problems such ...
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Due to the incorrect proportions between the exploitation and exploration phases, the whale optimization algorithm (WOA) gets stuck into the local optima, which causes premature convergence. To address this issue, qua...
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This paper presents an innovative approach employing persistence-based clustering in Riemannian manifolds within evolutionary computation algorithms to address multi-modal optimization problems. The proposed framework...
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