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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 6
  3. Swarm Intelligence : Volume 6, Issue 3, September 2012
  4. An experimental analysis of design choices of multi-objective ant colony optimization algorithms
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Swarm Intelligence : Volume 11
Swarm Intelligence : Volume 10
Swarm Intelligence : Volume 9
Swarm Intelligence : Volume 8
Swarm Intelligence : Volume 7
Swarm Intelligence : Volume 6
Swarm Intelligence : Volume 6, Issue 4, December 2012
Swarm Intelligence : Volume 6, Issue 3, September 2012
A competitive clustering particle swarm optimizer for dynamic optimization problems
An experimental analysis of design choices of multi-objective ant colony optimization algorithms
Training feedforward neural networks with dynamic particle swarm optimisation
Swarm Intelligence : Volume 6, Issue 2, June 2012
Swarm Intelligence : Volume 6, Issue 1, March 2012
Swarm Intelligence : Volume 5
Swarm Intelligence : Volume 4
Swarm Intelligence : Volume 3
Swarm Intelligence : Volume 2
Swarm Intelligence : Volume 1

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An experimental analysis of design choices of multi-objective ant colony optimization algorithms

Content Provider SpringerLink
Author López Ibáñez, Manuel Stützle, Thomas
Copyright Year 2012
Abstract There have been several proposals on how to apply the ant colony optimization (ACO) metaheuristic to multi-objective combinatorial optimization problems (MOCOPs). This paper proposes a new formulation of these multi-objective ant colony optimization (MOACO) algorithms. This formulation is based on adding specific algorithm components for tackling multiple objectives to the basic ACO metaheuristic. Examples of these components are how to represent multiple objectives using pheromone and heuristic information, how to select the best solutions for updating the pheromone information, and how to define and use weights to aggregate the different objectives. This formulation reveals more similarities than previously thought in the design choices made in existing MOACO algorithms. The main contribution of this paper is an experimental analysis of how particular design choices affect the quality and the shape of the Pareto front approximations generated by each MOACO algorithm. This study provides general guidelines to understand how MOACO algorithms work, and how to improve their design.
Starting Page 207
Ending Page 232
Page Count 26
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 6
Issue Number 3
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2012-07-04
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Ant colony optimization Multi-objective optimization Multi-objective traveling salesman problem Experimental analysis Artificial Intelligence (incl. Robotics) Computer Communication Networks Communications Engineering, Networks ApplicationMathematics/Computational Methods of Engineering Computer Systems Organization and Communication Networks
Content Type Text
Resource Type Article
Subject Artificial Intelligence
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