태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Minimizing cost while maximizing comfort while buying a car, and maximizing performance whilst minimizing fuel consumption and emission of pollutants of a vehicle are examples of multi-objective optimization problems involving two and three objectives, respectively. In practical problems, there can be more than three objectives.
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
This repository contains the database of the FEM simulation of axially impacted various configurations of the square crash boxes. This database records the impact of the structural and crash test parameters on the various crashworthiness objectives.
The dataset contains two Pareto-fronts: - The Pareto-front for the 2-objective problem - The Pareto-front for the 3-objective problem Each Pareto-front contains a set of points, with coordinates given by their objectives. The dataset also contains 1 possible action sequence that leads to this point. If there are multiple possible paths leading to the same point, only 1 was kept.
The following files contains the simulation inputs and outputs for conducting the multi-objetive optimization of thermal comfort and dyalight with the Response Surface Methodology. This files feed are needed for running the R script/code as well as the datasets are contained in the Github repository.
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.