Studying the Influence of the Objective Balancing Parameter in the Performance of a Multi-Objective Ant Colony Optimization Algorithm

Several multi-objective ant colony optimization (MOACO) algorithms use a parameter λ to balance the importance of each one of the objectives in the search. In this paper we have studied two different schemes of application for that parameter: keeping it constant, or changing its value during the algorithm running, in order to decide the configuration which yields the best set of solutions. We have done it considering our MOACO algorithm, named hCHAC, and two other algorithms from the literature, which have been adapted to solve the same problem. The experiments show that the use of a variable value for λ yields a wider Pareto set, but keeping a constant value for this parameter let to find better results for any objective.

[1]  Thomas Stützle,et al.  Ant Colony Optimization and Swarm Intelligence , 2008 .

[2]  Rolf Drechsler,et al.  Applications of Evolutionary Computing, EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings , 2008, EvoWorkshops.

[3]  Thomas Stützle,et al.  The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances , 2003 .

[4]  Wolfgang Banzhaf,et al.  Advances in Artificial Life , 2003, Lecture Notes in Computer Science.

[5]  Daniel Merkle,et al.  Bi-Criterion Optimization with Multi Colony Ant Algorithms , 2001, EMO.

[6]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[7]  Francisco Herrera,et al.  A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP , 2007, Eur. J. Oper. Res..

[8]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .

[9]  Juan Julián Merelo Guervós,et al.  Influence of parameters on the performance of a MOACO algorithm for solving the bi-criteria military path-finding problem , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[10]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

[11]  Juan Julián Merelo Guervós,et al.  hCHAC-4, an ACO Algorithm for Solving the Four-Criteria Military Path-finding Problem , 2007, NICSO.

[12]  Juan Julián Merelo Guervós,et al.  Comparing ACO Algorithms for Solving the Bi-criteria Military Path-Finding Problem , 2007, ECAL.

[13]  Benjamín Barán,et al.  A Multiobjective Ant Colony System for Vehicle Routing Problem with Time Windows , 2003, Applied Informatics.

[14]  Juan Julián Merelo Guervós,et al.  Enhancing a MOACO for Solving the Bi-criteria Pathfinding Problem for a Military Unit in a Realistic Battlefield , 2007, EvoWorkshops.

[15]  Oscar Cordón,et al.  An Empirical Analysis of Multiple Objective Ant Colony Optimization Algorithms for the Bi-criteria TSP , 2004, ANTS Workshop.