Voronoi-based archive sampling for robust optimisation

Doherty, Kevin; Alyahya, Khulood; Fieldsend, Jonathan E.; Akman, Ozgur E.

doi:10.1145/3205651.3205768

Cited by 2 publications

(1 citation statement)

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“…The LOOCV can detect the uncertain area of landscape and at the same time it might obtain a better solution. The Voronoi diagram, which has been demonstrated to be effective in robust optimization [21], provides a promising area for local search in the proposed algorithm. The detail of the proposed framework will be introduced in the next section.…”

Section: Literature Reviewmentioning

confidence: 99%

Voronoi-based Efficient Surrogate-assisted Evolutionary Algorithm for Very Expensive Problems

Hao

Huang

Liu

et al. 2019

2019 IEEE Congress on Evolutionary Computation (CEC)

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Very expensive problems are very common in practical system that one fitness evaluation costs several hours or even days. Surrogate assisted evolutionary algorithms (SAEAs) have been widely used to solve this crucial problem in the past decades. However, most studied SAEAs focus on solving problems with a budget of at least ten times of the dimension of problems which is unacceptable in many very expensive realworld problems. In this paper, we employ Voronoi diagram to boost the performance of SAEAs and propose a novel framework named Voronoi-based efficient surrogate assisted evolutionary algorithm (VESAEA) for very expensive problems, in which the optimization budget, in terms of fitness evaluations, is only 5 times of the problems dimension. In the proposed framework, the Voronoi diagram divides the whole search space into several subspace and then the local search is operated in some potentially better subspace. Additionally, in order to trade off the exploration and exploitation, the framework involves a global search stage developed by combining leave-one-out cross-validation and radial basis function surrogate model. A performance selector is designed to switch the search dynamically and automatically between the global and local search stages. The empirical results on a variety of benchmark problems demonstrate that the proposed framework significantly outperforms several state-ofart algorithms with extremely limited fitness evaluations. Besides, the efficacy of Voronoi-diagram is furtherly analyzed, and the results show its potential to optimize very expensive problems.

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Section: Literature Reviewmentioning

confidence: 99%