Quantitative Analysis and Performance Study of Ant Colony Optimization Models Applied to Multi-mode Resource Constraint Project Scheduling Problems

Abstract

Constraint Satisfaction Problems (CSP) belongs to this kind of traditional NP-hard problems with a high impact in both, research and industrial domains. However, due to the complexity that CSP problems exhibit, researchers are forced to use heuristic algorithms for solving the problems in a reasonable time. One of the most famous heuristic al- gorithms is Ant Colony Optimization (ACO) algorithm. The possible utilization of ACO algorithms to solve CSP problems requires the de- sign of a decision graph where the ACO is executed. Nevertheless, the classical approaches build a graph where the nodes represent the vari- able/value pairs and the edges connect those nodes whose variables are different. In order to solve this problem, a novel ACO model have been recently designed. The goal of this paper is to analyze the performance of this novelty algorithm when solving Multi-Mode Resource-Constraint Satisfaction Problems. Experimental results reveals that the new ACO model provides competitive results whereas the number of pheromones created in the system is drastically reduced.

Publication
International Conference on Harmony Search Algorithm
Antonio Gonzalez-Pardo
Antonio Gonzalez-Pardo
PhD Computer Science

Lecturer at the Computer Science Department. Main research interests are related to Computational Intelligence and Metaheuristics applied to Social Networks Analysis, and the optimization of graph-based problems.