Learning from the opposite: Strategies for Ants that solve multidimensional Knapsack problem

This work presents different opposite learning strategies for Ant Knapsack, an ant based algorithm for the Multidimensional Knapsack Problem. We propose to include a previous opposite learning phase to Ant Knapsack, for discarding regions of the search space. This opposite knowledge is then used by...

Full description

Saved in:

Bibliographic Details
Published in:	2016 IEEE Congress on Evolutionary Computation (CEC) pp. 193 - 200
Main Authors:	Rojas-Morales, Nicolas, Riff, R. Maria-Cristina, Montero, U. Elizabeth
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-07-2016
Subjects:	Benchmark testing Electronic mail Evolutionary computation Fixtures Optimization Search problems Urban areas
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	This work presents different opposite learning strategies for Ant Knapsack, an ant based algorithm for the Multidimensional Knapsack Problem. We propose to include a previous opposite learning phase to Ant Knapsack, for discarding regions of the search space. This opposite knowledge is then used by Ant Knapsack for solving the original problem. The objective is to improve the search process of Ant Knapsack maintaining its original design. We present three strategies which differ on how the solutions can be constructed on a opposite way. The results obtained are promising and encourage to use this approach for solving other problems.
DOI:	10.1109/CEC.2016.7743795