Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms

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...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 17; no. 7; pp. 1283 - 1299
Main Authors: Ahmed, Faez, Deb, Kalyanmoy
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer-Verlag 01.07.2013
Springer Nature B.V
Subjects:
Artificial Intelligence
Barriers
Computational Intelligence
Control
Elitism
Engineering
Genes
Genetic algorithms
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Multiple objective analysis
Optimization
Optimization algorithms
Optimization techniques
Path planning
Representations
Robotics
Safety
Smoothness
Soft computing
Sorting algorithms
Path smoothness
NSGA-II
Potential field
Multi-objective path planning
Path length
Path safety
Genetic algorithms
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary: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.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-012-0964-8