Comparison of Single and Multi-Population Evolutionary Algorithm for Path Planning in Navigation Situation

In this paper a comparison of single and multi-population evolutionary algorithm is presented. Tested algorithms are used to determine close to optimal ship paths in collision avoidance situation. For this purpose a path planning problem is defined. A specific structure of the individual path and fitness function is presented. Principle of operation of single-population and multi-population evolutionary algorithm is described. Using presented algorithms the simulations on three close to real sea environments were performed. Regardless of the test situation constant time simulation was maintained. Obtained results are presented in graphical form (sequences of successive stages of the simulation) and in form of table in which the values of fitness function for best individual in each simulation were compared. Undertaken research allow to select evolutionary algorithm that, assuming constant simulation time, will determine a better path in close to real collision avoidance situation at sea.

[1]  Chee-Keng Yap,et al.  Algorithmic and geometric aspects of robotics , 1987 .

[2]  Zbigniew Michalewicz,et al.  Modeling of ship trajectory in collision situations by an evolutionary algorithm , 2000, IEEE Trans. Evol. Comput..

[3]  Roman Smierzchalski,et al.  Trajectory Planning for Ship in Collision Situations at Sea by Evolutionary Computation , 1997 .

[4]  Michael E. Wall,et al.  Galib: a c++ library of genetic algorithm components , 1996 .

[5]  Qutaibah M. Malluhi,et al.  Advances in Intelligent Systems and Computing , 2015 .

[6]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[7]  R. Śmierzchalski,et al.  Evolutionary Hierarchical Agent Decision Support System For Marine Traffic Coordination , 2012 .

[8]  Ł. Kuczkowski,et al.  Selection Pressure in the Evolutionary Path Planning Problem , 2014 .

[9]  Piotr Kolendo,et al.  Comparison of Selection Schemes in Evolutionary Method of Path Planning , 2011, ICCCI.

[10]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[11]  Ł. Kuczkowski,et al.  Mean crossover in evolutionary path planning method for maritime collision avoidance , 2012 .

[12]  Piotr Kolendo,et al.  Distributed Evolutionary Algorithm for Path Planning in Navigation Situation , 2013 .

[13]  Piotr Kolendo,et al.  The Niching Mechanism in the Evolutionary Method of Path Planning , 2013, ICAISC.

[14]  P. Kolendo,et al.  Experimental Research on Evolutionary Path Planning Algorithm with Fitness Function Scaling for Collision Scenarios , 2011 .

[15]  Piotr Kolendo,et al.  Fitness function scaling in the evolutionary method of path planning , 2011, 2011 IEEE International Symposium on Industrial Electronics.

[16]  Masanori Ito,et al.  Collision avoidance of moving obstacles for ship with genetic algorithm , 2000, 6th International Workshop on Advanced Motion Control. Proceedings (Cat. No.00TH8494).

[17]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[18]  Zbigniew Michalewicz,et al.  Path Planning in Dynamic Environments , 2005, Innovations in Robot Mobility and Control.

[19]  Patrick Xuechun Zhao,et al.  A Method Based on Genetic Algorithm for Anti-ship Missile Path Planning , 2009, 2009 International Joint Conference on Computational Sciences and Optimization.

[20]  Micha Sharir,et al.  Algorithmic motion planning , 2004, Handbook of Discrete and Computational Geometry, 2nd Ed..

[21]  Arthur C. Sanderson,et al.  Multi-dimensional path planning using evolutionary computation , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[22]  Roman Smierzchalski,et al.  Ships' domains as collision risk at sea in the evolutionary method of trajectory planning , 2005, Information Processing and Security Systems.

[23]  Ł. Kuczkowski,et al.  Extinction Event Concepts for the Evolutionary Algorithms , 2012 .