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Ant Colony Optimizer: Theory, Literature Review, and Application in AUV Path Planning

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Nature-Inspired Optimizers

Part of the book series: Studies in Computational Intelligence ((SCI,volume 811))

Abstract

This chapter starts with the inspiration and main mechanisms of one of the most well-regarded combinatorial optimization algorithms called Ant Colony Optimizer (ACO). This algorithm is then employed to find the optimal path for an AUV. In fact, the problem investigated is a real-world application of the Traveling Salesman Problem (TSP).

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References

  1. Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: A new meta-heuristic. In Proceedings of the 1999 Congress on Evolutionary Computation, 1999 CEC 99 (Vol. 2, pp. 1470–1477). IEEE.

    Google Scholar 

  2. Dorigo, M., & Birattari, M. (2011). Ant colony optimization. In Encyclopedia of Machine Learning (pp. 36–39). Springer, Boston, MA.

    Google Scholar 

  3. Grass, P. P. (1959). La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la thorie de la stigmergie: Essai d’interprtation du comportement des termites constructeurs. Insectes sociaux, 6(1), 41–80.

    Google Scholar 

  4. Dorigo, M., Bonabeau, E., & Theraulaz, G. (2000). Ant algorithms and stigmergy. Future Generation Computer Systems, 16(8), 851–871.

    Article  Google Scholar 

  5. Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29–41.

    Google Scholar 

  6. Stützle, T., & Hoos, H. H. (1996). Improving the ant system: A detailed report on the MAXMIN Ant System. FG Intellektik, FB Informatik, TU Darmstadt, Germany, Tech. Rep. AIDA9612.

    Google Scholar 

  7. Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66.

    Article  Google Scholar 

  8. Papadimitriou, C. H., & Steiglitz, K. (1998). Combinatorial optimization: Algorithms and complexity. Courier Corporation.

    Google Scholar 

  9. Dorigo, M., & Stützle, T. (2003). The ant colony optimization metaheuristic: Algorithms, applications, and advances. In Handbook of metaheuristics (pp. 250–285). Springer, Boston, MA.

    Google Scholar 

  10. Stützle, T. (2009, April). Ant colony optimization. In International Conference on Evolutionary Multi-Criterion Optimization (pp. 2). Springer, Berlin, Heidelberg.

    Google Scholar 

  11. Stützle, T., Lpez-Ibnez, M., Pellegrini, P., Maur, M., De Oca, M. M., Birattari, M., & Dorigo, M. (2011). Parameter adaptation in ant colony optimization. In Autonomous Search (pp. 191–215). Springer, Berlin, Heidelberg.

    Google Scholar 

  12. Randall, M., & Lewis, A. (2002). A parallel implementation of ant colony optimization. Journal of Parallel and Distributed Computing, 62(9), 1421–1432.

    Article  Google Scholar 

  13. Stützle, T., & Hoos, H. H. (2000). MAXMIN ant system. Future Generation Computer Systems, 16(8), 889–914.

    Article  Google Scholar 

  14. Shahzad, F., Baig, A. R., Masood, S., Kamran, M., & Naveed, N. (2009). Opposition-based particle swarm optimization with velocity clamping (OVCPSO). In Advances in Computational Intelligence (pp. 339–348). Springer, Berlin, Heidelberg.

    Google Scholar 

  15. Sharvani, G. S., Ananth, A. G., & Rangaswamy, T. M. (2012). Analysis of different pheromone decay techniques for ACO based routing in ad hoc wireless networks. International Journal of Computer Applications, 56(2),

    Google Scholar 

  16. Socha, K. (2004, September). ACO for continuous and mixed-variable optimization. In International Workshop on Ant Colony Optimization and Swarm Intelligence (pp. 25–36). Springer, Berlin, Heidelberg.

    Google Scholar 

  17. Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3), 1155–1173.

    Article  MathSciNet  Google Scholar 

  18. Blum, C. (2005). Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2(4), 353–373.

    Article  Google Scholar 

  19. Hoffman, K. L., Padberg, M., & Rinaldi, G. (2013). Traveling salesman problem. In Encyclopedia of Operations Research and Management Science (pp. 1573–1578). Springer US.

    Google Scholar 

  20. Reinelt, G. (1991). TSPLIBA traveling salesman problem library. ORSA Journal on Computing, 3(4), 376–384.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, 4111, Australia

    Seyedali Mirjalili, Jin Song Dong & Andrew Lewis

  2. Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore

    Jin Song Dong

Authors
  1. Seyedali Mirjalili
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  2. Jin Song Dong
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  3. Andrew Lewis
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Correspondence to Seyedali Mirjalili .

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Editors and Affiliations

  1. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Seyedali Mirjalili

  2. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Jin Song Dong

  3. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia

    Andrew Lewis

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Mirjalili, S., Song Dong, J., Lewis, A. (2020). Ant Colony Optimizer: Theory, Literature Review, and Application in AUV Path Planning. In: Mirjalili, S., Song Dong, J., Lewis, A. (eds) Nature-Inspired Optimizers. Studies in Computational Intelligence, vol 811. Springer, Cham. https://doi.org/10.1007/978-3-030-12127-3_2

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