Abstract
(Hybrid) metaheuristics such as simulated annealing, genetic algorithms, or extremal optimization play a most prominent role in global optimization. The performance of these algorithms and their respective sampling behavior during the search process are themselves interesting problems. Here, we show that a combination of two approaches – namely Energy Landscape Paving (ELP) and Stochastic Tunneling (STUN) – can overcome known problems of other Metropolis-sampling-based procedures. We show on grounds of non-equilibrium statistical mechanics and empirical evidence on the synergistic advantages of this combined approach and discuss simulations for a complex optimization problem.
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Hamacher, K. (2013). A New Hybrid Metaheuristic – Combining Stochastic Tunneling and Energy Landscape Paving. In: Blesa, M.J., Blum, C., Festa, P., Roli, A., Sampels, M. (eds) Hybrid Metaheuristics. HM 2013. Lecture Notes in Computer Science, vol 7919. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38516-2_9
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DOI: https://doi.org/10.1007/978-3-642-38516-2_9
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