Abstract
In this study, three Genetic Algorithms (GAs) are applied to the Three-dimensional Multi-pipe Routing problem. A Standard GA, an Incremental GA, and a Coevolutionary GA are compared. Variable length pipelines are built by letting a virtual robot move in space according to evolved, fixed length command lines and allocate pipe segments along its route. A relative and an absolute encoding of the command lines are compared. Experiments on three proposed benchmark problems show that the GAs taking advantage of the natural problem decomposition; Coevolutionary GA, and Incremental GA outperform Standard GA, and that the relative encoding works better than the absolute encoding. The methods, the results, and the relevant parameter settings are discussed.
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References
Eiben, A., Schoenauer, M.: Evolutionary computing. Arxiv preprint cs/0511004 (2005)
Oduguwa, V., Tiwari, A., Roy, R.: Evolutionary computing in manufacturing industry: an overview of recent applications. Applied Soft Computing Journal 5(3), 281–299 (2005)
Potter, M., Jong, K.: Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents. Evolutionary Computation 8(1), 1–29 (2000)
Potter, M., De Jong, K.: A Cooperative Coevolutionary Approach to Function Optimization. In: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)
Husbands, P., Mill, F.: Simulated co-evolution as the mechanism for emergent planning and scheduling. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 264–270. Morgan Kaufmann Publishers, San Francisco (1991)
Neill, C., Laplante, P.: Requirements engineering: The state of the practice. IEEE software, 40–45 (2003)
Torresen, J.: Incremental evolution of a signal classification hardware architecture for prosthetic hand control. International Journal of Knowledge-based and Intelligent Engineering Systems 12(3), 187–199 (2008)
Gomez, F., Miikkulainen, R.: Incremental evolution of complex general behavior. Adaptive Behavior 5(3), 317–342 (1997)
Garder, L., Høvin, M.: Robot gaits evolved by combining genetic algorithms and binary hill climbing, pp. 1165–1170 (2006)
Park, J., Storch, R.: Pipe-routing algorithm development: case study of a ship engine room design. Expert Systems with Applications 23(3), 299–309 (2002)
Guirardello, R., Swaney, R.: Optimization of process plant layout with pipe routing. Computers and Chemical Engineering 30(1), 99–114 (2005)
Norvig, P., Russell, S.: Artificial intelligence: a modern approach. Prentice-Hall, Englewood Cliffs (2003)
Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE transactions on Systems Science and Cybernetics 4(2), 100–107 (1968)
Dijkstra, E.: A note on two problems in connexion with graphs. Numerische mathematik 1(1), 269–271 (1959)
Rubin, F.: The Lee path connection algorithm. IEEE Transactions on computers 100(23), 907–914 (1974)
Hightower, D.: A solution to line-routing problems on the continuous plane. In: Proceedings of the 6th annual conference on Design Automation, pp. 1–24. ACM, New York (1969)
Qian, X., Ren, T., Wang, C.: A survey of pipe routing design. In: Control and Decision Conference, CCDC 2008, pp. 3994–3998 (2008) (Chinese)
Ito, T.: A genetic algorithm approach to piping route path planning. Journal of Intelligent Manufacturing 10(1), 103–114 (1999)
Kim, D., Corne, D., Ross, P.: Industrial plant pipe-route optimisation with genetic algorithms. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 1012–1021. Springer, Heidelberg (1996)
Sandurkar, S., Chen, W.: GAPRUS genetic algorithms based pipe routing using tessellated objects. Computers in Industry 38(3), 209–223 (1999)
Wang, H., Zhao, C., Yan, W., Feng, X.: Three-dimensional Multi-pipe Route Optimization Based on Genetic Algorithms. International Federation for Information Processing-publications-IFIP 207, 177 (2006)
Baker, J.: Reducing bias and inefficiency in the selection algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application table of contents, pp. 14–21. L. Erlbaum Associates Inc., Hillsdale (1987)
Eiben, A., Smith, J.: Introduction to evolutionary computing. Springer, Heidelberg (2003)
Furuholmen, M., Glette, K., Hovin, M., Torresen, J.: Scalability, generalization and coevolution–experimental comparisons applied to automated facility layout planning. In: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pp. 691–698. ACM, New York (2009)
Furuholmen, M., Glette, K., Hovin, M., Torresen, J.: Coevolving Heuristics for The Distributors Pallet Packing Problem. In: Proceedings of the IEEE Congress on Evolutionary Computation (2009)
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Furuholmen, M., Glette, K., Hovin, M., Torresen, J. (2010). Evolutionary Approaches to the Three-dimensional Multi-pipe Routing Problem: A Comparative Study Using Direct Encodings. In: Cowling, P., Merz, P. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2010. Lecture Notes in Computer Science, vol 6022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12139-5_7
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