Search · four archives
Search · four archives
15 papers · ranked by Valyu relevance
Ahmed A. Zaki Diab, Ashraf M. Abdelhamid, Hamdy M. Sultan
This paper provides six metaheuristic algorithms, namely Fast Cuckoo Search (FCS), Salp Swarm Algorithm (SSA), Dynamic control Cuckoo search (DCCS), Gradient-Based Optimizer (GBO), Northern Goshawk Optimization (NGO), Opposition Flow Direction Algorithm (OFDA) to efficiently solve the optimal power flow (OPF) issue.…
Mohamed A. Mokhtar, Mohamed Fathy, Yasser A. Dahab, Emad A. Sayed
In modern machine learning, optimization algorithms are crucial; they steer the training process by skillfully navigating through complex, high-dimensional loss landscapes. Among these, stochastic gradient descent with momentum (SGDM) is widely adopted for its ability to accelerate convergence in shallow regions.…
Shikun Chen, Zebin Huang, Wenlong Zheng, Yuanchao Liu
Mathematical optimization is fundamental across many scientific and engineering applications. While data-driven models like gradient boosting and random forests excel at prediction tasks, they often lack mathematical regularity, being non-differentiable or even discontinuous. These models are commonly used to predict…
Aline C. Soterroni, Roberto L. Galski, Marluce C. Scarabello, Fernando M. Ramos
'Fernando M. Ramos'] In this work, the q-Gradient (q-G) method, a q-version of the Steepest Descent method, is presented. The main idea behind the q-G method is the use of the negative of the q-gradient vector of the objective function as the search direction. The q-gradient vector, or simply the q-gradient, is a…
Esraa Hassan, Mahmoud Y. Shams, Noha A. Hikal, Samir Elmougy
Optimization algorithms are used to improve model accuracy. The optimization process undergoes multiple cycles until convergence. A variety of optimization strategies have been developed to overcome the obstacles involved in the learning process. Some of these strategies have been considered in this study to learn more…
Mrinal Kanti Rajak, Rajen Pudur
This paper presents a novel Mitochondrial Energy Production Optimization (MEPO) algorithm for enhancing grid-connected inverter control under weak grid conditions. The proposed bio-inspired approach addresses critical challenges in maintaining power quality and system stability in low Short Circuit Ratio (SCR)…
Mohammad Dehghani, Štěpán Hubálovský, Pavel Trojovský, Wojciech Kempa + 1 more
'Wojciech Kempa' 'Iwona Paprocka'] Numerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a…
Simone Carlo Surace, Jean-Pascal Pfister, Wulfram Gerstner, Johanni Brea + 1 more
'Johanni Brea' 'Francis Ouellette'] This is a PLOS Computational Biology Education paper. The idea that the brain functions so as to minimize certain costs pervades theoretical neuroscience. Because a cost function by itself does not predict how the brain finds its minima, additional assumptions about the optimization…
Manuel S. Alvarez-Alvarado, Francisco E. Alban-Chacón, Erick A. Lamilla-Rubio, Carlos D. Rodríguez-Gallegos + 1 more
'Erick A. Lamilla-Rubio' 'Carlos D. Rodríguez-Gallegos' 'Washington Velásquez'] Based on the behavior of the quantum particles, it is possible to formulate mathematical expressions to develop metaheuristic search optimization algorithms. This paper presents three novel quantum-inspired algorithms, which scenario is a…
Javier Bernal, Jose Torres-Jimenez
SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller’s scaled conjugate gradient algorithm, the latter a variation of the traditional…
Chen Jianqiang, Zhang Feng, Ding Xuhai, Li Yong’en + 6 more
To enhance the 3D inversion accuracy of transient electromagnetic (TEM) prospecting, a TEM 3D visualization inversion method combining Particle Warm Optimization-Nonlinear Conjugate Gradient(PSO-NLCG) hybrid optimization method and adaptive regularization method is proposed. Based on establish the regularized inversion…
Ali Fozooni, Osman Kamari, Mostafa Pourtalebiyan, Masoud Gorgich + 2 more
'Mohammad Khalilzadeh' 'Amin Valizadeh'] In this study, we evaluate several nongradient (evolutionary) search strategies for minimizing mathematical function expressions. We developed and tested the genetic algorithms, particle swarm optimization, and differential evolution in order to assess their general efficacy in…
Steven A. Frank, Antonio M. Scarfone
Diverse learning algorithms, optimization methods, and natural selection share a common mathematical structure despite their apparent differences. Here, I show that a simple notational partitioning of change by the Price equation reveals a universal force-metric-bias (FMB) law: $Δθ=(Mf+b+ξ)$. The force $f$ drives…
Kai Zhang, Zengfei Wang, Liming Zhang, Jun Yao + 2 more
'Lixiang Li'] In this paper, we investigate the application of a new method, the Finite Difference and Stochastic Gradient (Hybrid method), for history matching in reservoir models. History matching is one of the processes of solving an inverse problem by calibrating reservoir models to dynamic behaviour of the…
Reza Toushmalani
The purpose of this study was to compare the performance of two methods for gravity inversion of a fault. First method [Particle swarm optimization (PSO)] is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish…