Abstract
This paper will address an innovative bio-inspired algorithm able to incrementally grow decision graphs in multiple directions for discrete multi-objective optimisation. The algorithm takes inspiration from the slime mould Physarum Polycephalum, an amoeboid organism that in its plasmodium state extends and optimizes a net of veins looking for food. The algorithm is here used to solve multi-objective Traveling Salesman and Vehicle Routing Problems selected as representative examples of multi-objective discrete decision making problems. Simulations on selected test cases showed that building decision sequences in two directions and adding a matching ability (multi-directional approach) is an advantageous choice if compared with the choice of building decision sequences in only one direction (unidirectional approach). The ability to evaluate decisions from multiple directions enhances the performance of the solver in the construction and selection of optimal decision sequences.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: optimisation by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B 26(1), 29–41 (1996)
Chong, C.S., Low, M.Y.H., Sivakumar, A.I., Gay, K.L.: A bee colony optimisation algorithm to job shop scheduling. In: Proceedings of the Winter IEEE Simulation Conference, WSC 2006, pp. 1954–1961 (2006)
Sayadi, M.K., Ramezanian, R., Ghaffari-Nasab, N.: A discrete firefly meta-heuristic with local search for makespan minimisation in permutation flow shop scheduling problems. International Journal of Industrial Engineering Computations 1(1), 1–10 (2010)
Yang, X.-S., Deb, S.: Cuckoo search via Levy flights. In: World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, pp. 210–214. IEEE (2009)
Nakagaki, T., Yamada, H., Toth, A.: Maze-Solving by an Amoeboid Organism. Nature 407, 470 (2000)
Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, D.P., Fricker, M.D., Yumiki, K., Kobayashi, R., Nakagaki, T.: Rules for Biologically Inspired Adaptive Network Design. Science 439, 327 (2010)
Adamatzky, A., MartÃnez, G.J., Chapa-Vergara, S.V., Asomoza-Palacio, R., Stephens, C.R.: Approximating Mexican highways with slime mould. Natural Computing 10(3), 1195–1214 (2011)
Hickey, D.S., Noriega, L.A.: Insights into Information Processing by the Single Cell Slime Mold Physarum Polycephalum. In: UKACC Control Conference (2008)
Tero, A., Yumiki, K., Kobayashi, R., Saigusa, T., Nakagaki, T.: Flow-Network Adaptation in Physarum Amoebae. Theory in Biosciences 127(2), 89–94 (2008)
Tero, A., Kobayashi, R., Nakagaki, T.: Physarum Solver: a Biologically Inspired Method of Road-Network Navigation. Physica: A Statistical Mechanics and its Applications 363(1), 115–119 (2006)
Alaya, I., Solnon, C., Ghedira, K.: Ant colony optimisation for multi-objective optimisation problems. In: 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2007, vol. 1, pp. 450–457 (2007)
Lopez-Ibanez, M.: Multi-objective Ant Colony optimisation. Diploma thesis, Intellectics Group, Computer Science Department, Technische Universitat Darmstadt, Germany (2004)
Garcia-Martinez, C., Cordon, O., Herrera, F.: A Taxonomy and an Empirical Analysis of Multiple Objective Ant Colony optimisation Algorithms for the Bi-Criteria TSP. European Journal of Operational Research 180, 116–148 (2007)
Masi, L., Vasile, M.: A multi-directional Modified Physarum Solver for Optimal Discrete Decision Making. In: Proceedings of International Conference on Bio-Inspired Optimisation Methods and their Applications, BIOMA, Bohinj, Slovenia (2012)
Dorigo, M., Gambardella, L.M.: Ant Colonies for the Traveling Salesman Problem. BioSystems 43, 73–81 (1997)
Monismith Jr., D.R., Mayfield, B.E.: Slime Mold as a Model for Numerical optimisation. In: IEEE Swarm Intelligence Symposium, St. Louis MO, USA (2008)
TSPLIB, library of instances for Traveling Salesman and Vehicle Routing Problems, Ruprecht Karls Universitaet Heidelberg, http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/
Vasile, M., Zuiani, F.: MACS: An Agent-Based Memetic Multiobjective optimisation Algorithm Applied to Space Trajectory Design. Journal of Aerospace Engineering, Institution of Mechanical Engineers, Part G (September 2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Masi, L., Vasile, M. (2014). A Multi-Directional Modified Physarum Algorithm for Optimal Multi-Objective Discrete Decision Making. In: Schuetze, O., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III. Studies in Computational Intelligence, vol 500. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01460-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-01460-9_9
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-01459-3
Online ISBN: 978-3-319-01460-9
eBook Packages: EngineeringEngineering (R0)