ACOustic: A nature-inspired exploration indicator for ant colony optimization
A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens’ acoustics of their ant hosts.The parasites’ reaction r...
Saved in:
Main Authors: | Sagban, Rafid, Ku-Mahamud, Ku Ruhana, Abu Bakar, Muhamad Shahbani |
---|---|
格式: | Article |
語言: | English |
出版: |
Hindawi Publishing Corporation
2015
|
主題: | |
在線閱讀: | http://repo.uum.edu.my/14740/1/2.pdf http://repo.uum.edu.my/14740/ http://doi.org/10.1155/2015/392345 |
標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
相似書籍
-
Reactive memory model for ant colony optimization and its application to TSP
由: Sagban, Rafid, et al.
出版: (2014) -
Nature-inspired parameter controllers for ACO-based reactive search
由: Sagban, Rafid, et al.
出版: (2015) -
An exploration technique for the interacted multiple ant colonies optimization framework
由: Aljanaby, Alaa, et al.
出版: (2010) -
Hybrid ant colony optimization for grid computing
由: Abdul Nasir, Husna Jamal, et al.
出版: (2009) -
Ant colony optimization algorithm for load balancing in grid computing
由: Ku-Mahamud, Ku Ruhana, et al.
出版: (2012)