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 |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Publishing Corporation
2015
|
Subjects: | |
Online Access: | http://repo.uum.edu.my/14740/1/2.pdf http://repo.uum.edu.my/14740/ http://doi.org/10.1155/2015/392345 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Reactive memory model for ant colony optimization and its application to TSP
by: Sagban, Rafid, et al.
Published: (2014) -
Nature-inspired parameter controllers for ACO-based reactive search
by: Sagban, Rafid, et al.
Published: (2015) -
An exploration technique for the interacted multiple ant colonies optimization framework
by: Aljanaby, Alaa, et al.
Published: (2010) -
Hybrid ant colony optimization for grid computing
by: Abdul Nasir, Husna Jamal, et al.
Published: (2009) -
Ant colony optimization algorithm for load balancing in grid computing
by: Ku-Mahamud, Ku Ruhana, et al.
Published: (2012)