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

作     者：Ahmed, Faez Deb, Kalyanmoy 

作者机构：Indian Inst Technol Dept Mech Engn Kanpur 208016 Uttar Pradesh India Aalto Univ Sch Econ Dept Informat & Serv Econ Helsinki 00100 Finland 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2013年第17卷第7期

页      面：1283-1299页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：JC Bose National Fellowship Department of Science and Technology, Government of India, under SERC-Engineering Sciences scheme [SR/S3/MERC/091/2009] Academy of Finland 

主　　题：Multi-objective path planning Potential field Path length Path safety Path smoothness NSGA-II Genetic algorithms 

摘      要：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.