By Per Kristian Lehre, Carsten Witt (auth.), Luca Di Gaspero, Andrea Schaerf, Thomas Stützle (eds.)
Metaheuristics were a really lively examine subject for greater than 20 years. in this time many new metaheuristic thoughts were devised, they've been experimentally verified and stronger on demanding benchmark difficulties, they usually have confirmed to be very important instruments for tackling optimization initiatives in a great number of functional purposes. In different phrases, metaheuristics are these days confirmed as one of many major seek paradigms for tackling computationally challenging difficulties. nonetheless, there are numerous study demanding situations within the quarter of metaheuristics. those demanding situations variety from extra basic questions about theoretical houses and function promises, empirical set of rules research, the potent configuration of metaheuristic algorithms, ways to mix metaheuristics with different algorithmic suggestions, in the direction of extending the on hand innovations to take on ever more difficult problems.
This edited quantity grew out of the contributions provided on the 9th Metaheuristics overseas convention that was once held in Udine, Italy, 25-28 July 2011. The convention comprised 117 shows of peer-reviewed contributions and three invited talks, and it's been attended via 169 delegates. The chapters which are accumulated during this e-book exemplify contributions to numerous of the study instructions defined above.
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91(2), 201–213 (2002) 9. : Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009. In: Proceedings of the Conference on Genetic and Evolutionary Computation, pp. 1689–1696, New York, NY, USA (2010) 10. : Testing heuristics: We have it all wrong. J. C. Barbosa, Heder S. S. Barreto 11. : Guidelines for reporting results of computational experiments. Report of the ad hoc committee. Math. Program. 49, 413–425 (1991) 12. : Benchmarking derivative-free optimization algorithms.
Table 5 presents (1) the original ranking, (2) the ranking obtained by removing p01 , p02 , p04 , and p09 , and (3) the ranking resulting from removing only p17 . Recall that, in all cases, test-problems p20 and p22 have been excluded from the suite. 2 0 1 10 log(τ) 100 Fig. C. Barbosa, Heder S. S. 2 0 1 10 log(τ) 100 Fig. 9: Performance profiles for the results of the CEC 2006 competition when the test-problem p17 is excluded Table 5: Comparison between the original ranking (with respect to the AUC) and the ranking obtained when different test-problems are removed from the suite 1 Standard s01 AUC from Fig.
1 Introduction The performance of a metaheuristic algorithm largely depends on the expertise in tuning the algorithm’s control parameters. For example, a simulated annealing algorithm yields good solutions only if several parameters such as initial temperature, cooling factor, number of iterations and so on are properly tuned. However, finding the best combination of parameter settings is a tedious and time-consuming task. net L. Di Gaspero et al. 1007/978-1-4614-6322-1 3, © Springer Science+Business Media New York 2013 37 38 Aldy Gunawan, Hoong Chuin Lau, and Elaine Wong In recent years, several automated approaches for finding good parameter settings have been proposed.