Ant Colony Optimization for Solving the Quadratic Assignment Problem

2015 ◽  
Author(s):  
Alfredo Reyes Montero ◽  
Abraham Sanchez Lopez
Keyword(s):  
Ant Colony Optimization ◽  
Assignment Problem ◽  
Quadratic Assignment Problem ◽  
Ant Colony ◽  
Quadratic Assignment

scholarly journals A Hybrid Ant Colony Algorithm for Quadratic Assignment Problem

2013 ◽  
Vol 7 (1) ◽  
pp. 51-54 ◽  
Author(s):  
Guo Hong
Keyword(s):  
Ant Colony Optimization ◽  
Assignment Problem ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Quadratic Assignment Problem ◽  
Ant Colony ◽  
Quadratic Assignment ◽  
Optimal Solutions ◽  
Combinatorial Optimization Problems

Quadratic assignment problem (QAP) is one of fundamental combinatorial optimization problems in many fields. Many real world applications such as backboard wiring, typewriter keyboard design and scheduling can be formulated as QAPs. Ant colony algorithm is a multi-agent system inspired by behaviors of real ant colonies to solve optimization problems. Ant colony optimization (ACO) is one of new bionic optimization algorithms and it has some characteristics such as parallel, positive feedback and better performances. ACO has achieved in solving quadratic assignment problems. However, its solution quality and its computation performance need be improved for a large scale QAP. In this paper, a hybrid ant colony optimization (HACO) has been proposed based on ACO and particle swarm optimization (PSO) for a large scale QAP. PSO algorithm is combined with ACO algorithm to improve the quality of optimal solutions. Simulation experiments on QAP standard test data show that optimal solutions of HACO are better than those of ACO for QAP.

scholarly journals ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Rafid Sagban ◽  
Ku Ruhana Ku-Mahamud ◽  
Muhamad Shahbani Abu Bakar
Keyword(s):  
Ant Colony Optimization ◽  
Fitness Landscape ◽  
Optimization Problems ◽  
Quadratic Assignment Problem ◽  
Travelling Salesman Problem ◽  
Optimization Algorithms ◽  
Ant Colony ◽  
Quadratic Assignment ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Optimization Algorithms

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 results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.

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