Analyzing the impact of MOACO components: An algorithmic study on the multi-objective shortest path problem

Authors:
Leonardo C. T. Bezerra;Elizabeth F. G. Goldbarg;Marco C. Goldbarg;Luciana S. Buriol
Affiliations:
Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil;Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil;Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil;Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

Citing 12
Cited 0

MAX-MIN Ant system

Future Generation Computer Systems
Bi-Criterion Optimization with Multi Colony Ant Algorithms

EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Ant Colony Optimization for Multi-Objective Optimization Problems

ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
A comparison of solution strategies for biobjective shortest path problems

Computers and Operations Research
CHAC, A MOACO algorithm for computation of bi-criteria military unit path in the battlefield: Presentation and first results

International Journal of Intelligent Systems - Special Issue on Nature Inspired Cooperative Strategies for Optimization
An ant colony optimization algorithm for the bi-objective shortest path problem

Applied Soft Computing
GRACE: a generational randomized ACO for the multi-objective shortest path problem

EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Ant colony system: a cooperative learning approach to the traveling salesman problem

IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach

IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review

IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	12.05

Visualization

Abstract

Multi-objective Ant Colony Optimization (MOACO) algorithms have been successfully applied to several multi-objective combinatorial optimization problems (MCOP) over the past decade. Recently, we proposed a MOACO algorithm named GRACE for the multi-objective shortest path (MSP) problem, confirming the efficiency of such metaheuristic for this MCOP. In this paper, we investigate several extensions of GRACE, proposing several single and multi-colony variants of the original algorithm. All variants are compared on the original set of instances used for proposing GRACE. The best-performing variants are also assessed using a new benchmark containing 300 larger instances with three different underlying graph structures. Experimental evaluation shows one of the variants to produce better results than the others, including the original GRACE, thus improving the state-of-the-art of MSP.