The authors outline a new visual tool that can help patients assess the benefits and risks of different treatments.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1475654 | PMC |
| http://dx.doi.org/10.1371/journal.pmed.0030137 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Analyzing vehicle path optimization using an improved genetic algorithm in the presence of stochastic perturbation matter.
Sci Rep
October 2024
School of Finance, Fujian Business University, Fujian, 350012, China.
By analyzing the influence of stochastic perturbation matters on vehicle path optimization, a perturbation scheduling model for logistics and distribution with a carbon tax mechanism is established under the premise of time window variation and load capacity constraints. Herein, we propose an enhanced Genetic Algorithm (GA) based on a Gaussian matrix mutation (GMM) operator, which maintains the diversity of the population while speeding up the algorithm's convergence. The model builds a Gaussian probability matrix using the site positional order distribution characteristics implied in the original site data information, and applies the Gaussian probability matrix to individual gene mutations using a roulette-wheel-selection method; thus, the study guarantees the genetic diversity of the population while guiding it to evolve in the high-fitness direction.
View Article and Find Full Text PDFHybrid Optimization Path Planning Method for AGV Based on KGWO.
Sensors (Basel)
September 2024
Wuhan Second Ship Design and Research Institute, Wuhan 430205, China.
To address the path planning problem for automated guided vehicles (AGVs) in challenging and complex industrial environments, a hybrid optimization approach is proposed, integrating a Kalman filter with grey wolf optimization (GWO), as well as incorporating partially matched crossover (PMX) mutation operations and roulette wheel selection. Paths are first optimized using GWO, then refined with Kalman filter corrections every ten iterations. Moreover, roulette wheel selection guides robust parent path selection, while an elite strategy and partially matched crossover (PMX) with mutation generate diverse offspring.
View Article and Find Full Text PDFComparative analysis of the gazelle Optimizer and its variants.
Heliyon
September 2024
Department of Electronics, Information and Communication Engineering, Kangwon National University, Samcheok 25913, Republic of Korea.
The Gazelle Optimization Algorithm (GOA) is an innovative nature-inspired metaheuristic algorithm, designed to mimic the agile and efficient hunting strategies of gazelles. Despite its promising performance in solving complex optimization problems, there is still a significant scope for enhancing its efficiency and robustness. This paper introduces several novel variants of GOA, integrating adaptive strategy, Levy flight strategy, Roulette wheel selection strategy, and random walk strategy.
View Article and Find Full Text PDFResearch on MEC computing offload strategy for joint optimization of delay and energy consumption.
Math Biosci Eng
June 2024
Kunming Iron & Steel Holding Co., Ltd. Kunming 650302, China.
The decision-making process for computational offloading is a critical aspect of mobile edge computing, and various offloading decision strategies are strongly linked to the calculated latency and energy consumption of the mobile edge computing system. This paper proposes an offloading scheme based on an enhanced sine-cosine optimization algorithm (SCAGA) designed for the "edge-end" architecture scenario within edge computing. The research presented in this paper covers the following aspects: (1) Establishment of computational resource allocation models and computational cost models for edge computing scenarios; (2) Introduction of an enhanced sine and cosine optimization algorithm built upon the principles of Levy flight strategy sine and cosine optimization algorithms, incorporating concepts from roulette wheel selection and gene mutation commonly found in genetic algorithms; (3) Execution of simulation experiments to evaluate the SCAGA-based offloading scheme, demonstrating its ability to effectively reduce system latency and optimize offloading utility.
View Article and Find Full Text PDFColor matching of bicomponent (PET/PTT) filaments with high performances using genetic algorithm.
Sci Rep
May 2024
National Engineering School of Monastir, University of Monastir, Monastir, Tunisia.
In recent years, a great interest has focused on the use of bicomponent filaments in several high-performance textile articles such as swimwear, sportswear and even high-quality denim. To dye fabrics containing these filaments, it is necessary to establish appropriate dye recipes allowing to obtain desired shades. In this article, we developed a genetic algorithm to optimize the color matching step of these bicomponent filaments, especially (PET/PTT) filaments.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!