A framework for self-tuning optimization algorithm

@article{Yang2013AFF,
  title={A framework for self-tuning optimization algorithm},
  author={Xin-She Yang and Suash Deb and Martin Loomes and Mehmet Karamanoğlu},
  journal={Neural Computing and Applications},
  year={2013},
  volume={23},
  pages={2051-2057}
}
The performance of any algorithm will largely depend on the setting of its algorithm-dependent parameters. The optimal setting should allow the algorithm to achieve the best performance for solving a range of optimization problems. However, such parameter tuning itself is a tough optimization problem. In this paper, we present a framework for self-tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself. Using the firefly algorithm as an example, we show that… 

12 A Framework for Self-Tuning Algorithms

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This chapter presents a framework for self-tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself.

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References

SHOWING 1-10 OF 26 REFERENCES

Parameter tuning for configuring and analyzing evolutionary algorithms

Bat algorithm: a novel approach for global engineering optimization

A new nature‐inspired metaheuristic optimization algorithm, called bat algorithm (BA), based on the echolocation behavior of bats is introduced, and the optimal solutions obtained are better than the best solutions obtained by the existing methods.

Firefly algorithm, stochastic test functions and design optimisation

This paper shows how to use the recently developed firefly algorithm to solve non-linear design problems and proposes a few new test functions with either singularity or stochastic components but with known global optimality and thus they can be used to validate new optimisation algorithms.

Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems

The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area and the optimal solutions obtained are mostly far better than the best solutions obtained by the existing methods.

Firefly Algorithms for Multimodal Optimization

Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms.

Engineering optimisation by cuckoo search

This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions to apply the CS algorithm to solve engineering design optimisation problems, including the design of springs and welded beam structures.

Engineering Optimization: An Introduction with Metaheuristic Applications

The author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms.