Palavras-chave
Mathematical programming
Metaheuristics
Como Citar
Resumo
This undergraduate research aims to study metaheuristics for solving mathematical programming problems. In optimization's context, metaheuristics are strategies which use problem specific knowledge to, stochastically use solutions worse than the current in order to avoid local maximum or minimum. The literature about metaheuristics is huge and continues in expansion, mostly by its commercial interest, once most real problems are far too large to be solved by exact algorithms. This project considered a wide variety of metaheuristics and focused on a particular one: The Ant Colony Optmization applied to the Symmetric Travelling Salesman Problem (TSP).
Referências
Dorigo, M.;Stützle, T. Ant Colony Optimization. Italy: MIT Press, 2004.
Dorigo, M. Optimization, learning and natural algorithms. Italy: PhD Thesis, Politecnico di Milano, 1992.
Dorigo, M.; Maniezzo, V.; Colorni, A. Ant system: optimization by a colony of cooperating agentes. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), v. 26, n. 1, p. 29-41, 1996.
Gendreau, M.; Potvin, J. Handbook of Metaheuristics. London: Springer, second edition, 2010.
Hurst, Harold Edwin. Long-term storage capacity of reservoirs. Trans. Amer. Soc. Civil Eng., v. 116, p. 770-799, 1951.
Mandelbrot, Benoit B.; Wallis, James R. Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence. Water Resources Research, v. 5, n. 6, p. 967-988, 1969.
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Copyright (c) 2019 Revista dos Trabalhos de Iniciação Científica da UNICAMP