By Per Kristian Lehre, Carsten Witt (auth.), Luca Di Gaspero, Andrea Schaerf, Thomas Stützle (eds.)
Metaheuristics were a really lively study subject for greater than 20 years. in this time many new metaheuristic innovations were devised, they've been experimentally demonstrated and better on demanding benchmark difficulties, they usually have confirmed to be vital instruments for tackling optimization projects in quite a few functional functions. In different phrases, metaheuristics are these days validated as one of many major seek paradigms for tackling computationally demanding difficulties. nonetheless, there are a good number of study demanding situations within the quarter of metaheuristics. those demanding situations diversity from extra basic questions about theoretical homes and function promises, empirical set of rules research, the potent configuration of metaheuristic algorithms, ways to mix metaheuristics with different algorithmic strategies, in the direction of extending the on hand ideas to take on ever tougher problems.
This edited quantity grew out of the contributions offered 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 through 169 delegates. The chapters which are accumulated during this booklet exemplify contributions to a number of of the learn instructions defined above.
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Yes (Convergence=MaxRejections) No Below [stop temperature]? Yes (Convergence = Stop Temperature) No Reset tries counter Reset success counter Increment computations counter No Exceed [max computations]? Yes (Convergence=MaxComputations) stop Fig. 1: Flow chart of simulated annealing algorithm Real-World Parameter Tuning Using Factorial Design with Parameter Decomposition 43 Table 1: Parameter definition Parameters (pi ) Abbreviation Definition Max successes Max tries Max computations Max rejections Max change Success Tries Comp Reject Change Maximum number of successes within one temperature Maximum number of tries within one temperature Maximum number of solutions generated Maximum number of consecutive rejections Maximum change in a variable value when generating a new solution Number of tries to generate a feasible solution Factor to reduce the temperature by during each Temperature change A value to depict the strictness of the oracle function in accepting a new solution that has an objective value worse than the current one.
For example, the CPLEX solver has 76 parameters that affect the search process . In such problems, parameter search space reduction can be important in order for automated tuning to become computationally feasible. By decomposing the parameters into disjoint partitions and tuning them separately, the parameter search space will be significantly reduced. Lau and Xiao , for example, decompose the parameters set into disjoint graphs based on the correlation among the parameters, and the approach was used for tuning a genetic algorithm (GA) for the bandwidth minimization problem (BMP).
In order to test the significance of the planar model, interaction and curvature tests have to be conducted. The interaction test is used to test the significance of any interaction between parameters by looking at the significance of the estimated coefficient between two parameters. The curvature test tests whether the planar model is adequate to represent the local response function. The surface of parameters can still be approximated by a planar model as long as the existence of either interaction or curvature is not significant.