• Corpus ID: 238634124

Parameter Tuning Strategies for Metaheuristic Methods Applied to Discrete Optimization of Structural Design

@article{Negrin2021ParameterTS,
  title={Parameter Tuning Strategies for Metaheuristic Methods Applied to Discrete Optimization of Structural Design},
  author={Iv{\'a}n A. Negrin and Dirk Roose and Ernesto Chagoy'en},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.06186}
}
This paper presents several strategies to tune the parameters of metaheuristic methods for (discrete) design optimization of reinforced concrete (RC) structures. A novel utility metric is proposed, based on the area under the average performance curve. The process of modelling, analysis and design of realistic RC structures leads to objective functions for which the evaluation is computationally very expensive. To avoid costly simulations, two types of surrogate models are used. The first one… 

References

SHOWING 1-10 OF 51 REFERENCES
Performance Assessment of Metaheuristic Algorithms for Structural Optimization Taking into Account the Influence of Control Parameters
TLDR
This contribution proposes a method to assess the performance (i.e. the ability to find the best known solution and the associated computational cost) of a metaheuristic algorithm that takes into account the influence of its control parameters.
Performance Assessment of Metaheuristic Algorithms for Structural Optimization Taking Into Account the Influence of Algorithmic Control Parameters
TLDR
The new performance assessment method is demonstrated for the genetic algorithm in matlab R2018b, applied to seven common structural optimization test problems, where it successfully detects unimportant parameters while identifying well-performing values for the important parameters.
A Survey of Automatic Parameter Tuning Methods for Metaheuristics
TLDR
A new classification (or taxonomy) of automatic parameter tuning methods is introduced according to the structure of tuning methods and these methods are classified into three categories: 1) simple generate-evaluate methods; 2) iterative generate- evaluating methods; and 3) high-level generate- evaluate methods.
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
TLDR
The development of CALIBRA is described, a procedure that attempts to find the best values for up to five search parameters associated with a procedure under study and is able to find parameter values that either match or improve the performance of the procedures resulting from using the parameter values suggested by their developers.
Finding optimal algorithmic parameters using a mesh adaptive direct search
TLDR
The flexibility of the mesh adaptive direct search (mads) in identifying locally optimal algorithmic parameters is demonstrated by devising a general framework for parameter tuning and specializing it to the identification of locally optimal trust-region parameters in unconstrained optimization.
Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization
TLDR
This special session is devoted to the approaches, algorithms and techniques for solving real parameter single objective optimization without making use of the exact equations of the test functions.
Parameter Tuning of Evolutionary Algorithms: Generalist vs. Specialist
TLDR
It is demonstrated that REVAC can also tune an EA to a set of problems (a whole test suite) and obtain robust, rather than problem-tailored, parameter values and an EA that is a ‘generalist,rather than a’ ‘specialist’.
Sequential Model-Based Optimization for General Algorithm Configuration
TLDR
This paper extends the explicit regression models paradigm for the first time to general algorithm configuration problems, allowing many categorical parameters and optimization for sets of instances, and yields state-of-the-art performance.
Fine-tuning a tabu search algorithm with statistical tests
TLDR
Although the focus of this work is to improve a particular tabu search algorithm developed for solving a telecommunications network design problem, the implications are quite general and the same ideas and procedures can easily be adapted and applied to othertabu search algorithms as well.
A Racing Algorithm for Configuring Metaheuristics
TLDR
A procedure that empirically evaluates a set of candidate configurations by discarding bad ones as soon as statistically sufficient evidence is gathered against them and allows to focus on the most promising ones is proposed.
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