.png){kind=icon}
.png){kind=icon}
Return
A clustering-based symbiotic organisms search algorithm for high-dimensional optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2020.106722.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposed a fast metaheuristic method for high-dimensional optimizations problem with only one-control parameter in the setting. Essentially, the innovation of the proposed method is to apply automatic k-means clustering on the initial solutions of symbiotic organisms search to create subpopulations. Only the selected elite solutions in each cluster to interact with one another across clusters in the proposed model. This new elite solution searching process can be considered as a combination of local and global searching based on the solution clusters. The proposed method was compared to six representative methods in 28 benchmark problems and 10 composition problems. Also, the proposed method was also compared with four clustering-based metaheuristic methods. The experimental results show that the proposed model is more efficient in its computation and has a better searching quality. For high-dimensional problems, the performances of the proposed method was compared with the original symbiotic organisms search up to 1000 dimensions. The results show that the proposed method can alleviate the dimensionality effect to produce better solution quality with relatively fast computation. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Metaheuristic
Optimization
Symbiotic organisms search
Clustering
High-dimensional problems
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W