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Fitness switching genetic algorithm for solving combinatorial optimization problems with rare feasible solutions

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Abstract

The goal of meta-heuristic algorithms such as genetic algorithm is to explore the search space of the combinatorial optimization problems efficiently to locate the optimal solutions, the feasible solutions with the best output values. However, typical meta-heuristic algorithms implicitly assume that the feasible solutions for the given problems can be generated easily, and they can fail to solve the problems with rare feasible solutions in effective manner. In this context, this paper aims to introduce the maze-type shortest path problem as an example of the combinatorial optimization problem with rare feasible solutions and to propose the fitness switching genetic algorithm for solving it. The maze-type shortest path problem is characterized by the maze-type network that contains many dead-ends, and the conventional genetic algorithms based on the population of feasible paths are not appropriate for finding the optimal path in such networks. On the contrary, this paper introduces the fitness switching and fitness leveling operations for maintaining the population of both feasible and infeasible paths during the search procedure. In addition, the infeasible paths are randomly modified by the simple local search of the proposed algorithm to find the feasible paths more quickly. The experiment results show that the proposed algorithm can address the issues in the combinatorial optimization problems with rare feasible solutions very effectively.

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References

  1. Ahn CW, Ramakrishna RS (2002) A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Trans Evolut Comput 6(6):566–579

    Article  Google Scholar 

  2. Baba N, Hanada H (1994) Genetic algorithm applied to maze passing problem of mobile robot-a comparison with the learning performance of the hierarchical structure stochastic automata. In: The 1994 IEEE International Conferece on Neural Networks, pp 2690–2695

  3. Carrick C, MacLeod K (2013) An evaluation of genetic algorithm solutions in optimization and machine learning, In: The 21st Annual Conference Canadian Association for Information Science, pp 224–231

  4. Chitra C, Subbaraj P (2012) A nondominated sorting genetic algorithm solution for shortest path routing problem in computer networks. Expert Syst Appl 39(1):1518–1525

    Article  Google Scholar 

  5. Dao S, Marian R (2011) Modeling and optimization of precedence-constrained production sequencing and scheduling for multiple production lines using genetic algorithm. Comput Technol Appl 2(6):487–499

    Google Scholar 

  6. Dolgov D, Thrun S, Montemerlo M, Diebel J (2010) Path planning for autonomous vehicles in unknown semi-structured environments. Int J Robot Res 29(5):485–501

    Article  Google Scholar 

  7. Drexl M, Irnich S (2014) Solving elementary shortest-path problems as mixed-integer programs. OR Spectrum 36(2):281–296

    Article  MathSciNet  MATH  Google Scholar 

  8. Fu L, Sun D, Rilett LR (2006) Heuristic shortest path algorithms for transportation applications: state of the art. Comput Oper Res 33(11):3324–3343

    Article  MATH  Google Scholar 

  9. Gen M, Cheng R, Wang Q (1997) Genetic algorithms for solving shortest path problems. In: The IEEE International Conference on Evolutionary Computation, pp 401–406

  10. Glover F (1990) Tabu search: a tutorial. Interfaces 20(4):74–94

    Article  Google Scholar 

  11. Gordon VS, Matley Z (2004) Evolving sparse direction maps for maze pathfinding. In: The Congress on Evolutionary Computation, pp 835–838

  12. Gupta S, Garg ML (2009) An improved genetic algorithm based on adaptive repair operator for solving the knapsack problem. J Comput Sci 5(8):544–547

    Article  Google Scholar 

  13. Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4(2):100–107

    Article  Google Scholar 

  14. Hassanzadeh R, Mahdavi I, Mahdavi-Amiri N, Tajdin A (2013) A genetic algorithm for solving fuzzy shortest path problems with mixed fuzzy arc lengths. Math Comput Model 57(1):84–99

    Article  MathSciNet  MATH  Google Scholar 

  15. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press

  16. Inagaki J, Haseyama M, Kitajima H (1999) A genetic algorithm for determining multiple routes and its applications. In: The 1999 IEEE International Symposium on Circuits and Systems, pp 137–140

  17. Ismail AT, Sheta A, Al-Weshah M (2008) A mobile robot path planning using genetic algorithm in static environment. J Comput Sci 4(4):341–344

    Article  Google Scholar 

  18. Johnson DB (1973) A note on Dijkstra’s shortest path algorithm. J ACM 20(3):385–388

    Article  MATH  Google Scholar 

  19. Kabir MM, Shahjahan M, Murase K (2011) A new local search based hybrid genetic algorithm for feature selection. Neurocomputing 74(17):2914–2928

    Article  Google Scholar 

  20. Kameyama K (2009) Particle swarm optimization—a survey. IEICE Trans Inf Syst 92(7):1354–1361

    Article  Google Scholar 

  21. Koulamas C, Antony SR, Jaen R (1994) A survey of simulated annealing applications to operations research problem. Omega 22(1):41–56

    Article  Google Scholar 

  22. Lawler EL, Wood DE (1966) Branch-and-bound methods: a survey. Oper Res 14(4):699–719

    Article  MathSciNet  MATH  Google Scholar 

  23. Li S, Ding M, Cai C, Jiang L (2010) Efficient path planning method based on genetic algorithm combining path network. In: The 2010 Fourth International Conference on Genetic and Evolutionary Computing, pp 194–197

  24. Lin CH (2013) A rough penalty genetic algorithm for constrained optimization. Inf Sci 241:119–137

    Article  Google Scholar 

  25. Lipowski A, Lipowska D (2012) Roulette-wheel selection via stochastic acceptance. Phys A Stat Mech Appl 391(6):2193–2196

    Article  Google Scholar 

  26. Montańa JL, Alonso CL, Borges CE, De La Dehesa J (2011) Penalty functions for genetic programming algorithms. Comput Sci Appl ICCSA 2011:550–562

    Google Scholar 

  27. Moon C, Yun Y (2011) Genetic algorithm approach for precedence-constrained sequencing problems. J Intell Manuf 22(3):379–388

    Article  Google Scholar 

  28. Pattnaik SB, Mohan S, Tom VM (1998) Urban bus transit route network design using genetic algorithm. J Transport Eng 124(4):368–375

    Article  Google Scholar 

  29. Pehlivanoglu YV (2012) A new vibrational genetic algorithm enhanced with a Voronoi diagram for path planning of autonomous UAV. Aerosp Sci Technol 16(1):47–55

    Article  Google Scholar 

  30. Qiuqi R (2004) A gene-constrained genetic algorithm for solving shortest path problem. In: The 2004 7th International Conference on Signal Processing, pp 2510–2513

  31. Su S, Tsuchiya K (1993) Learning of a maze using a genetic algorithm. In: The International Conference on Industrial Electronics, Control, and Instrumentation, pp 376–379

  32. Srinivas M, Patnaik LM (1994) Genetic algorithms: a survey. Computer 27(6):17–26

    Article  Google Scholar 

  33. Wang H, Lu X, Zhang X, Wang Q, Deng Y (2014) A bio-inspired method for the constrained shortest path problem. Sci World J

  34. Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85

    Article  Google Scholar 

  35. Yang S, Cheng H, Wang F (2010) Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks. IEEE Trans Syst Man Cybern Part C Appl Rev 40(1):52–63

    Article  Google Scholar 

  36. Zhu X, Luo W, Zhu T (2014) An improved genetic algorithm for dynamic shortest path problems. In: The 2014 IEEE Congress on Evolutionary Computation, pp 2093–2100

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2012R1A1A1044834).

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Authors and Affiliations

  1. Department of Industrial and Management Systems Engineering, Dong-A University, Busan, Korea

    Jun Woo Kim

  2. Department of Game Engineering, Paichai University, Daejeon, Korea

    Soo Kyun Kim

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  1. Jun Woo Kim
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  2. Soo Kyun Kim
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Correspondence to Soo Kyun Kim.

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Kim, J.W., Kim, S.K. Fitness switching genetic algorithm for solving combinatorial optimization problems with rare feasible solutions. J Supercomput 72, 3549–3571 (2016). https://doi.org/10.1007/s11227-016-1687-x

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  • DOI: https://doi.org/10.1007/s11227-016-1687-x

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