Abstract
Optimization is used in diverse areas of science, technology and business. Metaheuristics are one of the common approaches for solving optimization problems. In this paper we propose a novel and functional metaheuristic, Fisherman Search Procedure (FSP), to solve combinatorial optimization problems, which explores new solutions using a combination of guided and local search. We evaluate the performance of FSP on a set of benchmark functions commonly used for testing global optimization methods. We compare FSP with other heuristic methods referenced in literature, namely Differential Evolution (DE), Particle Swarm Optimization (PSO) and Greedy Randomized Adaptive Search Procedures (GRASP). Results are analyzed in terms of successful runs, i.e., convergence on global minimum values, and time consumption, demonstrating that FSP can achieve very good performances in most of the cases. In 90% of the cases FSP is located among the two better results as for successful runs. On the other hand, with regard to time consumption, FSP shows similar results to PSO and DE, achieving the best and second best results for 82% of the test functions. Finally, FSP showed to be a simple and robust metaheuristic that achieves good solutions for all evaluated theoretical problems.
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References
Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Dordrecht (1997)
Kirkpartick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)
Calegari, P., Coray, G., Hertz, A., Kobler, D., Kuonen, P.: A Taxonomy of Evolutionary Algorithms in Combinatorial Optimization. Journal of Heuristics 5(2), 145–158 (1999)
Moscato, P.: Memetic Algorithms – A Short Introduction. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 219–234. McGraw-Hill, London (1999)
Glover, F.: Scatter Search and Path Relinking. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 297–316. McGraw-Hill, London (1999)
Dorigo, M., Gambardella, L.M.: Ant Colony System – A Cooperative Learning Approach to the Travelling Salesman Problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)
Eberhart, R.C., Kennedy, J.: A new Optimizer Using Particles Swarm Theory. In: 6th International Symposium on Micro Machine and Human Science (MHS 1995), pp. 39–43. IEEE (1995)
Resende, M.G.C., Ribeiro, C.C.: Greedy Randomized Adaptive Search Procedures. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 57, pp. 219–249. Springer, Heidelberg (2003)
Stützle, T.: Local Search Algorithms for Combinatorial Problems – Analysis, Algorithms and New Applications. DISKI – Dissertationen zur Künstliken Intelligenz, Infix (1999)
Stützle, T.: Iterated Local Search for the Quadratic Assignment Problem. Technical report, TU Darmstadt (1999)
Hansen, P., Mladenovic, N.: An Introduction to Variable Neighborhood Search. In: Voss, S., et al. (eds.) Metaheuristics – Advances and Trends in Local Search Paradigms for Optimization, pp. 433–458. Kluwer Academic Publishers (1999)
Molga, M., Smutnicki, C.: Test Functions for Optimization Needs. Technical report (2005), http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf
Shi, Y., Eberhart, R.: A Modified Particle Swarm Optimizer. In: IEEE World Congress on Computational Intelligence (WCCI 1998), pp. 69–73. IEEE (1998)
Clerc, M., Kennedy, J.: The Particle Swarm – Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical report TR-95-012, International Computer Science Institute, Berkeley, CA, USA (1995)
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Machado, O.J.A., Luna, J.M.F., Guadix, J.F.H., Morales, E.R.C. (2012). Fisherman Search Procedure. In: Pavón, J., Duque-Méndez, N.D., Fuentes-Fernández, R. (eds) Advances in Artificial Intelligence – IBERAMIA 2012. IBERAMIA 2012. Lecture Notes in Computer Science(), vol 7637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34654-5_30
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DOI: https://doi.org/10.1007/978-3-642-34654-5_30
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