Abstract
In this research, a new metaheuristic called Worm Optimization (WO) is proposed, based on the foraging behaviors of Caenorhabditis elegans (Worms). In particular, the algorithm will mimic the behaviors of worms including finding food, avoiding toxins, interchanging between solitary and social foraging styles, alternating between food exploiting and seeking, and entering a stasis stage. WO effectiveness is illustrated on the traveling salesman problem (TSP), a known NP-hard problem, and compared to well-known naturally inspired algorithms using existing TSP data. The computational results reflected the superiority of WO in all tested problems. Furthermore, this superiority improved as problem sizes increased, and WO attained the global optimal solution in all tested problems within a reasonable computational time.
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Arnaout J-P (2014) Worm optimization: a novel optimization algorithm inspired by C. Elegans. In: Proceedings of the 2014 international conference on industrial engineering and operations management, Indonesia, pp 2499–2505
Avery L, You YJ (2012) C. elegans feeding. In: WormBook D (ed) The C. elegans research community, WormBook. http://www.wormbook.org/chapters/www-feeding/feeding.pdf. Cited 21 May 2012
Basu S (2012) Tabu search implementation on traveling salesman problem and its variations: a literature survey. Am J Oper Res 2:163
Bersini H, Oury C, Dorigo M (1995) Hybridization of genetic algorithms. Tech. Rep. No. IRIDIA 95-22, IRIDIA, Universit Libre de Bruxelles, Belgium
Bongard J (2009) Biologically inspired computing. IEEE Comput 42:95–98
Brenner S (1974) The genetics of Caenorhabditis elegans. Genetics 77:71–94
Dorigo M, Birattari M (2007) Swarm intelligence. Scholarpedia 2:1462
Dorigo M, Gambardella LM (1997) Ant colonies for the travelling salesman problem. BioSystems 43:73–81
Eilon S, Watson-Gandy CDT, Christofides N (1969) Distribution management: mathematical modeling and practical analysis. Oper Res Q 20:37–53
Ferris H (2013) Caenorhabditis elegans. University of California, Davis. http://plpnemweb.ucdavis.edu/nemaplex/Taxadata/G900S2.htm. Cited 30 Nov 2013
Fisher RA (1960) The design of experiments. Hafner Publishing Company, New York
Hu PJ (2007) Dauer. In: WormBook (ed) The C. elegans research community, WormBook. http://www.wormbook.org/chapters/www-dauer/dauer.pdf. Cited 8 Aug 2007
Jabr F (2012) The connectome debate: is mapping the mind of a worm worth it? http://www.scientificamerican.com/article/c-elegans-connectome/
Krolak P, Felts W, Nelson J (1972) A man-machine approach toward solving the generalized truck-dispatching problem. Transp Sci 6:149–170
Larranaga P, Kuijpers CMH, Murga RH, Inza I, Dizdarevic S (1999) Genetic algorithms for the traveling salesman problem: a review of representations and operators. Artif Intell Rev 13:129–170
Lin F-T, Kao C-Y, Hsu C-C (1993) Applying the genetic approach to simulated annealing in solving some NP-hard problems. IEEE Trans Syst Man Cyber 23:1752–1767
Lockery S (2009) A social hub for worms. Nature 458:1124–1125
Macosko E, Pokala N, Feinberg E, Chalasani S, Butcher R, Clardy J, Bargmann C (2009) A hub-and-spoke circuit drives pheromone attraction and social behavior in C. elegans. Nature 458:1171–1176
Maupas E (1900) Modes et formes de reproduction des nematodes. Archives de Zoologie Exprimentale et Gnrale 8:463–624
NIST/SEMATECH (2008) e-Handbook of statistical methods. http://www.itl.nist.gov/div898/handbook/. Cited 25 Jun 2008
Oliver I, Smith D, Holland JR (1987) A study of permutation crossover operators on the travelling salesman problem. In: Grefenstette JJ (ed) Proceedings of 2nd international conference on genetic algorithms. Lawrence Erlbaum, Hillsdale, pp 224–230
Reinelt G (1994) The traveling salesman: computational solutions for TSP applications. Springer, Berlin
Reinhelt G: TSPLIB: a library of sample instances for the TSP (and related problems) from various sources and of various types, http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/
Ross P (1996) Taguchi techniques for quality engineering. McGraw Hill, New York
SchedulingResearch (2015) http://www.schedulingresearch.com. Accessed 1 Aug 2015
Shi X, Liang Y, Lee H, Lu C, Wang Q (2007) Particle swarm optimization-based algorithms for TSP and generalized TSP. Inform Process Lett 5:169–176
Shtonda BB, Avery L (2006) Dietary choice behavior in Caenorhabditis elegans. J Exp Biol 209:89–102
Taguchi G (1993) Taguchi methods: design of experiments. American Supplier Institute, Livonia, MI
Tsai CF, Tsai CW, Tseng C (2004) A new hybrid heuristic approach for solving large traveling salesman problem. Inform Sci 166:67–81
Whitley D, Starkweather T, Fuquay D (1989) Scheduling problems and travelling salesman: the genetic edge recombination operator. In: Schaffer JD (ed) Proceedings of the third international conference on genetic algorithms. Morgan Kaufmann, San Mateo, pp 133–140
Wood WB (1988) The Nematode Caenorhabditis elegans. Cold Spring Harbor Laboratory Press, New York
Xu J-X, Deng X (2012) Complex chemotaxis behaviors of C. elegans with speed regulation achieved by dynamic neural networks. In: Proceedings of the IEEE world congress on computational intelligence, Brisbane
Yang J, Wu C, Lee HP, Liang Y (2008) Solving traveling salesman problems with generalized chromosome genetic algorithm. Prog Nat Sci 18:887–892
Zhang Y, Lu H, Bargmann C (2005) Pathogenic bacteria induce aversive olfactory learning in Caenorhabditis elegans. Nature 438:179–184
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Arnaout, JP. (2016). Worm Optimization for the Traveling Salesman Problem. In: Rabadi, G. (eds) Heuristics, Metaheuristics and Approximate Methods in Planning and Scheduling. International Series in Operations Research & Management Science, vol 236. Springer, Cham. https://doi.org/10.1007/978-3-319-26024-2_11
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