21 papers · ranked by Valyu relevance
M. Emre Celebi, Hassan A. Kingravi, Patricio A. Vela
K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of the cluster centers. Numerous initialization methods have been proposed to address this problem. In this paper, we first present…
S. Westerhof, T. Hofman
This paper presents an optimization framework for Spatial Packaging of Interconnected Systems with Physical Interactions (SPI2) that addresses the geometric challenges of three-dimensional component placement and routing. While SPI2 generally includes physical interactions, this study isolates the spatial optimization…
Qian Li, Sanyang Liu, Xin‐She Yang
All metaheuristic optimization algorithms require some initialization, and the initialization for such optimizers is usually carried out randomly. However, initialization can have some significant influence on the performance of such algorithms. This paper presents a systematic comparison of 22 different initialization…
Mengyu Huang, Yuxing Zhong, Huiwen Yang, Jiazheng Wang + 3 more
'Bo Bai' 'Ling Shi'] The simplex method is one of the most fundamental technologies for solving linear programming (LP) problems and has been widely applied to different practical applications. In the past literature, how to improve and accelerate the simplex method has attracted plenty of research. One important way…
Jie You, Zhaoxuan Li, Junli Du, Praveen Kumar Donta
Gaussian mixture model (GMM) is a very useful tool, which is widely used in complex probability distribution modeling, such as data classification , image classification and segmentation , speech recognition , etc. The Gaussian mixture model is composed of K single Gaussian distributions. For a single Gaussian…
Jian Zhang, Jianan Sheng, Jiawei Lu, Ling Shen
The particle swarm optimization algorithm (PSO) is a meta-heuristic algorithm with swarm intelligence. It has the advantages of easy implementation, high convergence accuracy, and fast convergence speed. However, PSO suffers from falling into a local optimum or premature convergence, and a better performance of PSO is…
João Fé, Sérgio D. Correia, Slavisa Tomic, Marko Beko + 1 more
'Paolo Bellavista'] In the last decades, several swarm-based optimization algorithms have emerged in the scientific literature, followed by a massive increase in terms of their fields of application. Most of the studies and comparisons are restricted to high-level languages (such as MATLAB®) and testing methods on…
Carsten Gottschlich, Dominic Schuhmacher, Haipeng Peng
In the subsequent comparison of methods for constructing an initial feasible solution (stage one in the transportation algorithm), we take the following established procedures into account. If a method generates fewer than basis entries (degenerate case), we complement them by adding the right number of entries in such…
Jiuge Ren, David Sweet
Field experiments and computer simulations are effective but time-consuming methods of measuring the quality of engineered systems at different settings. To reduce the total time required, experimenters may employ Bayesian optimization, which is parsimonious with measurements, and take measurements of multiple settings…
Authors not listed
With the ever-increasing demand for atomistic structures representative of real-life systems as well as the ad-vent of exascale computers, it has now become necessary and possible to use advanced global optimization (GO) techniques to intelligently sample the potential energy surface (PES). Given the previous studies…
Mohammed Baragilly, Brian H Willis
Meta-analysis may be used to summarise a test’s accuracy. Often the sensitivity and specificity are the measures of interest and as these are correlated a bivariate random effects model is commonly used to fit the data. This model has five parameters and it may be optimised using a Newton-Raphson based algorithm…
Abdul Kader Kassoumeh, Zühal Kartal, Ahmet Arslan, Dragan Pamucar
This article introduces methods for initializing a single-trajectory-based metaheuristic, specifically a simulated annealing (SA) algorithm, using constructive heuristics. These methods are designed to target promising regions within the search space of an nondeterministic polynomial time (NP)-hard problem, namely the…
Authors not listed
This study presents a novel application of Multi-Objective Bayesian Optimization (MOBO) to enhance the formulation of flame-retardant polypropylene (PP) composites. Our goal was to optimize the chemical composition of intumescent polypropylene (PP) formulations by maximizing the Limiting Oxygen Index (LOI) and…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Loïc J. Azzalini, David Crompton, Gabriele M. T. D’Eleuterio, Frances Skinner + 1 more
Data assimilation techniques for state and parameter estimation are frequently applied in the context of computational neuroscience. In this work, we show how an adaptive variant of the unscented Kalman filter (UKF) performs on the tracking of a conductance-based neuron model. Unlike standard recursive filter…
Authors not listed
We present a novel, flexible framework for electronic structure interfaces designed for nonadiabatic dynamics simulations, implemented in Python 3 using concepts of object-oriented programming. This framework streamlines the development of new interfaces by providing a reusable and extendable code base. It supports the…
Kazunori D Yamada
In the deep learning era, a gradient descent method is the most common method to optimize parameters of neural networks. Among various mathematical optimization methods, a gradient descent method is the most naive method. Although controlling a learning rate of the method is necessary for quick convergence, the…
Thomas Lynn, Julio Ottino, Richard Lueptow, Paul Umbanhowar
Cut-and-shuffle mixing is an instructive candidate system with which to assess the potential of machine learning (ML) as an approach to solve difficult mixing problems. We focus on a specific subset of cut-and-shuffle systems, the one-dimensional interval exchange transform. This class of mixing operations is well…
Assem F. Alabu-Husain, Mostafa A. ElBahloul, Mahmoud M. Saafan, Eman M. El-Gendy
This study introduces the Pray Optimization Algorithm (POA), a novel metaheuristic inspired by the procedural rituals of Islamic pray, designed to solve complex engineering and robotic manipulator problems. The mathematical model is structured into three distinct phases: Phase I simulates searching for a suitable…
Alejandro F. Villaverde, Fabian Fröhlich, Daniel Weindl, Jan Hasenauer + 1 more
Mechanistic kinetic models usually contain unknown parameters, which need to be estimated by optimizing the fit of the model to experimental data. This task can be computationally challenging due to the presence of local optima and ill-conditioning. While a variety of optimization methods have been suggested to…
Authors not listed
Efficient and reliable identification of transition states (TS) is critical for reaction modelling. Among the approaches available, the combination of double-ended TS search with eigenvector-following, referred to as “hierarchical TS search”, is an effective tool to locate TSs starting from reactant and product…