Abstract
We present experimental results of applying various nature-inspired optimization techniques to real-world problems from the areas of diagnosis, configuration, planning, and pathfinding. The optimization techniques we investigate include the traditional Genetic Algorithm (GA), discrete (binary and integer-based) Particle Swarm Optimization (DPSO), relatively new Extremal Optimization (EO), and recently developed Raindrop Optimization (RO); all inspired by different aspects of the natural world. We present algorithm setup, issues with adapting the various optimization methods to the selected problems, and the emerging results produced by the methods.We consider the GA to be the baseline technique because of its robustness and widespread application. The major contribution of this chapter deals with the fact that DPSO, EO, and RO have never been applied to the majority of these selected problems, making this the first time most of these results have appeared in the literature.
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References
Bak, P., Sneppen, K.: Punctuated equilibrium and criticality in a simple model of evolution. Phys. Rev. Lett. 71(24), 4083–4086 (1993)
Bettinger, P., Sessions, J.: Spatial forest planning: to adopt, or not to adopt? J. For. 101(2), 24–29 (2003)
Bettinger, P., Chung, W.: The key literature of, and trends in, forest-level management planning in North America, 1950-2001. Int. For. Rev. 6, 40–50 (2004)
Bettinger, P., Zhu, J.: A new heuristic for solving spatially constrained forest planning problems based on mitigation of infeasibilities radiating outward from a forced choice. Silva Fennica 40(2), 315–333 (2006)
Boettcher, S., Percus, A.G.: Extremal optimization: methods derived from co-evolution. In: GECCO 1999: Proc. Genet. and Evol. Comput. Conf., pp. 825–832. Morgan Kaufmann, San Francisco (1999)
Boettcher, S., Percus, A.G.: Extremal optimization for graph partitioning. Phys. Rev. EÂ 64, 26114 (2001)
Chang, F.L., Potter, W.D.: A genetic algorithm approach to solving the battlefield communication network configuration problem. In: Yfantis, E.A. (ed.) Intell. Sys. Third Golden West Intern. Conf. Theory and Decision Library D, vol. 15. Kluwer, Dordrecht (1995)
Diaz-Gomez, P., Hougen, D.: Genetic algorithms for hunting snakes in hypercubes: fitness function analysis and open questions. In: Seventh ACIS Intern. Conf. on Softw. Eng., Artif. Intell., Netw., and Parallel/Distrib. Comput, SNPD 2006, pp. 389–394. IEEE Computer Society, Los Alamitos (2006)
Diaz-Gomez, P., Hougen, D.: The snake in the box problem: mathematical conjecture and a genetic algorithm approach. In: Cattolico, M. (ed.) Proc. 8th annu. conf. on Genet. and evol. comput., pp. 1409–1410. ACM Press, New York (2006b)
Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. Wiley and Sons, New York (2005)
Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Boston (1989)
Harary, F., Hayes, J.P., Wu, H.J.: A survey of the theory of hyper-cube graphs. Comput. Math. Appl. 15, 277–289 (1988)
Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975)
Kautz, W.H.: Unit-distance error-checking codes. IRE Trans. Electron. Comp. 7, 179–180 (1958)
Klee, V.: What is the maximum length of a d-dimensional snake? Am. Math. Mon. 77, 63–65 (1970)
Kennedy, J., Eberhart, R.: Swarm intelligence. Morgan Kaufmann, San Francisco (2001)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE Intern. Conf. on Neural Netw., pp. 1942–1948. IEEE Service Center, Piscataway (1995)
Kochut, K.J.: Snake-in-the-box codes for dimension 7. J. Comb. Math. Comb. Comput. 20, 175–185 (1996)
Liepins, G.E., Potter, W.D.: A Genetic Algorithm Approach to Multiple Fault Diagnosis. In: Davis, L. (ed.) Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)
Martin, M., Drucker, E., Potter, W.D.: GA, EO, and DPSO applied to the discrete network configuration problem. In: Proc. Intern. Conf. Genet. and Evol. Methods, GEM 2008, pp. 129–134 (2008) CD Paper ID: GEM3397
MSE, Mobile Subscriber Equipment System: Reference Guide for the US Army. GTE Tactical Systems, Taunton, MA (1990)
Peng, Y., Reggia, J.A.: A probabilistic causal model for diagnostic problem solving, part I: integrating symbolic causal inference with numeric probabilistic inference. IEEE Trans. Syst., Man, Cybern. 17(2), 146–162 (1987a)
Peng, Y., Reggia, J.A.: A probabilistic causal model for diagnostic problem solving, part II: diagnostic strategy. IEEE Trans. Syst., Man, Cybern. 17(3), 395–406 (1987b)
Potter, W.D., Pitts, R., Gillis, P., et al.: IDA-NET: an intelligent decision aid for battlefield communications network configuration. In: Proc. 8th IEEE Conf. on Artif. Intell. Appl (CAIA 1992), pp. 247–253. IEEE Computer Society Press, Los Alamitos (1992a)
Potter, W.D., Miller, J.A., Tonn, B.E., et al.: Improving the reliability of heuristic multiple fault diagnosis via the environmental conditioning operator. Appl. Intell. 2, 5–23 (1992b)
Pugh, J., Martinoli, A.: Discrete multi-valued particle swarm optimization. In: Proc. 2006 IEEE Swarm Intell. Symp., pp. 103–110 (2006)
Reggia, J.A., Nau, D., Wang, P.: Diagnostic expert systems based on a set covering model. Int. J. Man-Mach Stud. 19(5), 437–460 (1983)
de Sousa, F.L., Ramos, F.M., Paglione, P., et al.: New stochastic algorithm for design optimization. AIAA J. 41(9), 1808–1818 (2003)
Tuohy, D.R., Potter, W.D., Casella, D.A.: Searching for snake-in-the-box codes with evolved pruning models. In: Arabnia, H.R., Yang, J.Y., Yang, M.Q. (eds.) Proc. Int. Conf. Genet. and Evol. Methods (GEM 2007), pp. 3–9. CSREA Press (2007)
Zhu, J., Bettinger, P., Li, R.: Additional insight into the performance of a new heuristic for solving spatially constrained forest planning problems. Silva Fennica 41(4), 687–698 (2007)
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Potter, W.D. et al. (2009). Diagnosis, Configuration, Planning, and Pathfinding: Experiments in Nature-Inspired Optimization. In: Chiong, R., Dhakal, S. (eds) Natural Intelligence for Scheduling, Planning and Packing Problems. Studies in Computational Intelligence, vol 250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04039-9_11
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DOI: https://doi.org/10.1007/978-3-642-04039-9_11
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