.png){kind=icon}
.png){kind=icon}
Return
Fractal Decomposition Approach for Continuous Multi-Objective Optimization Problems
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3022866.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-objective optimization problems (MOPs) have been widely studied during the last decades. In this article, we present a new intrinsically parallel approach based on Fractal decomposition (FDA) to solve MOPs. The key contribution of the proposed approach is to divide recursively the decision space using hyperspheres. Two different methods were investigated: the first one is based on scalarization that has been distributed on a parallel multi-node architecture virtual environments and taking profit from the FDA's properties, while the second method is based on Pareto dominance sorting. A comparison with state of the art algorithms on different well known benchmarks shows the efficiency and the robustness of the proposed decomposition approaches.
Keywords:
Fractals
Pareto optimization
Linear programming
Electronic mail
Licenses
Sorting
Multi-objective optimization
large-scale optimization
metaheuristics
geometric fractal decomposition
local search
continuous optimization
containers
virtualization
Docker
Kubernetes
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W