By Per Kristian Lehre, Carsten Witt (auth.), Luca Di Gaspero, Andrea Schaerf, Thomas Stützle (eds.)
Metaheuristics were a really energetic study subject for greater than twenty years. in this time many new metaheuristic suggestions were devised, they've been experimentally validated and greater on not easy benchmark difficulties, they usually have confirmed to be very important instruments for tackling optimization initiatives in quite a few sensible purposes. In different phrases, metaheuristics are these days confirmed as one of many major seek paradigms for tackling computationally demanding difficulties. nonetheless, there are quite a few study demanding situations within the zone of metaheuristics. those demanding situations diversity from extra basic questions about theoretical houses and function promises, empirical set of rules research, the powerful configuration of metaheuristic algorithms, techniques to mix metaheuristics with different algorithmic innovations, in the direction of extending the on hand recommendations to take on ever tougher problems.
This edited quantity grew out of the contributions offered on the 9th Metaheuristics overseas convention that used to be held in Udine, Italy, 25-28 July 2011. The convention comprised 117 displays of peer-reviewed contributions and three invited talks, and it's been attended by means of 169 delegates. The chapters which are amassed during this publication exemplify contributions to a number of of the learn instructions defined above.
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7] proposed a procedure, based on DOE and gradient descent, to find parameter settings for vehicle routing heuristics. The drawbacks of the approach are that: the linear approximation of the response surface and the average setting might not be appropriate if the class of problems is too broad. AdensoD´ıaz and Laguna  developed CALIBRA, which employs a Taguchi fractional experimental design and a local search procedure to tune up to five parameters. CALIBRA only focuses on the linear assumption without examining interactions between parameters.
2 0 1 10 log(τ) 100 Fig. 2 0 1 10 100 log(τ) Fig. 4: Performance profiles for the results of the CEC’06 competition weighted by the dimension of the problems Using Performance Profiles for the Analysis and Design of Benchmark Experiments 29 problems can be excluded a priori from the analysis. The corresponding standard performance profiles are presented in Fig. 3. The performance profiles when the test-problems are weighted according to problem dimensionality (Eq. 3) and hardness (Eq. 5) are shown in Figs.
6b, the test-problems G and H are able to cluster the solvers in S in two groups, one of them achieving a higher performance on this test-problem than the other. Also, it can be seen that test-problem H has a higher discriminative power within each cluster of solvers. Besides visual inspection, one can also define a way to automatically identify the test-problem(s) with low discriminative power. For a given solver, one can compute the sum of the differences between the values of τ associated with each test-problem and the (ideal) even distribution along the τ axis.