Abstract
Stochastic Tunneling (STUN) is an optimization heuristic whose basic mechanism is based on reducing barriers for its search process between local optima via a non-linear transformation. Here, we hybridize STUN with the idea of Tabu Search (TS), namely, the avoidance of revisiting previously assessed solutions. This prevents STUN from inefficiently scan areas of the search space whose objective function values have already been “transformed away”. We introduce the novel idea of using a probabilistic data structure (Bloom filters) to store a (quasi-)infinite tabu history. Empirical results for a combinatorial optimization problem show superior performance. An analysis of the tabu list statistics shows the importance of this hybridization idea.
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Notes
- 1.
For general, random \(J_{ij}\) there exist one symmetry between up/down-spin states that eventually degenerates into two global solution dividing the search space in one half.
References
Binder, K., Young, A.: Spin glasses : experimental facts, theoretical concepts, and open questions. Rev. Mod. Phys. 58(4), 801–976 (1986)
Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970). http://doi.acm.org/10.1145/362686.362692
Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13(5), 533–549 (1986)
Glover, F.: Tabu search-part I. ORSA J. Comput. 1(3), 190–206 (1989)
Glover, F.: Tabu search-part II. ORSA J. Comput. 2(1), 4–32 (1990)
Hamacher, K.: Adaptation in stochastic tunneling global optimization of complex potential energy landscapes. Europhys. Lett. 74(6), 944–950 (2006)
Hamacher, K., Wenzel, W.: The scaling behaviour of stochastic minimization algorithms in a perfect funnel landscape. Phys. Rev. E 59(1), 938–941 (1999)
Hamacher, K.: A new hybrid metaheuristic – combining stochastic tunneling and energy landscape paving. In: Blesa, M.J., Blum, C., Festa, P., Roli, A., Sampels, M. (eds.) HM 2013. LNCS, vol. 7919, pp. 107–117. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38516-2_9
Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Simone, C., Diehl, M., Jünger, M., Mutzel, P., Reinelt, G.: Exact ground states of ising spin glasses: new experimental results with a branch-and-cut algorithm. J. Stat. Phys. 80, 487 (1995)
Strunk, T., et al.: Structural model of the gas vesicle protein GvpA and analysis of GvpA mutants in vivo. Mol. Microbiol. 81(1), 56–68 (2011)
Tarkoma, S., Rothenberg, C.E., Lagerspetz, E.: Theory and practice of bloom filters for distributed systems. IEEE Commun. Surv. Tutorials 14(1), 131–155 (2012)
Wenzel, W., Hamacher, K.: A Stochastic tunneling approach for global minimization. Phys. Rev. Lett. 82(15), 3003–3007 (1999)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)
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Hamacher, K. (2019). Hybridization of Stochastic Tunneling with (Quasi)-Infinite Time-Horizon Tabu Search. In: Blesa Aguilera, M., Blum, C., Gambini Santos, H., Pinacho-Davidson, P., Godoy del Campo, J. (eds) Hybrid Metaheuristics. HM 2019. Lecture Notes in Computer Science(), vol 11299. Springer, Cham. https://doi.org/10.1007/978-3-030-05983-5_9
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