Ant Colony vs. Genetic Multiobjective Route Planning in Dynamic Multi-hop Ridesharing

The multiobjective route planning problem in dynamic multi-hop ridesharing is considered to be NP-complete. Evolutionary computation has received a growing interest in solving the hard multiobjective optimization problems. In this study we investigate the behavior of different variants of the ant colony based approach for solving the multiobjective route planning problem and compare the performance of the different variants with the performance of a genetic algorithm recommended for solving the problem. Experimentation results indicate that the ant colony approach encounters poor performance in its native form and competes the genetic approach in some of its variants when combined with local search.