@Article{Ahmed2013, author={Ahmed, Faez and Deb, Kalyanmoy}, title={Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms}, journal={Soft Computing}, year={2013}, month={Jul}, day={01}, volume={17}, number={7}, pages={1283-1299}, abstract={A multi-objective vehicle path planning method has been proposed to optimize path length, path safety, and path smoothness using the elitist non-dominated sorting genetic algorithm---a well-known soft computing approach. Four different path representation schemes that begin their coding from the start point and move one grid at a time towards the destination point are proposed. Minimization of traveled distance and maximization of path safety are considered as objectives of this study while path smoothness is considered as a secondary objective. This study makes an extensive analysis of a number of issues related to the optimization of path planning task-handling of constraints associated with the problem, identifying an efficient path representation scheme, handling single versus multiple objectives, and evaluating the proposed algorithm on large-sized grids and having a dense set of obstacles. The study also compares the performance of the proposed algorithm with an existing GA-based approach. The evaluation of the proposed procedure against extreme conditions having a dense (as high as 91 {\%}) placement of obstacles indicates its robustness and efficiency in solving complex path planning problems. The paper demonstrates the flexibility of evolutionary computing approaches in dealing with large-scale and multi-objective optimization problems.}, issn={1433-7479}, doi={10.1007/s00500-012-0964-8}, url={https://doi.org/10.1007/s00500-012-0964-8} }