Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling

Ong, Yew Soon; Nair, Prasanth B.; Keane, Andrew

doi:10.2514/2.1999

Cited by 497 publications

(260 citation statements)

References 28 publications

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“…Controlled Random Search algorithms such as Simulated Annealing and Hide and Seek were later on developed, based on which new solutions are generated based on a sequence of probability distributions. Other developments which belong in this category are Genetic Algorithms, Particle Swarm Optimization, Memetic Algorithms and Tabu-search (Ong et al, 2003;Das and Suganthan, 2011;Pal et al, 2012;Sun et al, 2013). There exist several developments which extend the capabilities of stochastic methods to mixed-integer optimization, such as the work of Laguna et al (2014) which is developed to solve purely discrete variable problems with general constraints using scatter search.…”

Section: Stochastic Methodsmentioning

confidence: 99%

Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO

Boukouvala

Misener

Floudas

2016

European Journal of Operational Research

193

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This manuscript reviews recent advances in deterministic global optimization for Mixed-Integer Nonlinear Programming (MINLP), as well as Constrained DerivativeFree Optimization (CDFO). This work provides a comprehensive and detailed literature review in terms of significant theoretical contributions, algorithmic developments, software implementations and applications for both MINLP and CDFO. Both research areas have experienced rapid growth, with a common aim to solve a wide range of real-world problems. We show their individual prerequisites, formulations and applicability, but also point out possible points of interaction in problems which contain hybrid characteristics. Finally, an inclusive and complete test suite is provided for both MINLP and CDFO algorithms, which is useful for future benchmarking.

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Section: Stochastic Methodsmentioning

confidence: 99%

Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO

Boukouvala

Misener

Floudas

2016

European Journal of Operational Research

193

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“…In the context of evolutionary optimization surrogate models are used to provide a rough approximation to guide the global search, or a local accurate surrogate model is used to speedup the local search step, or a combination of both [9,10]. For instance, Zhou et al [11] apply a data parallel Gaussian Process for the global approximation and a (simple) Radial Basis Function (RBF) model for the local search.…”

Section: Surrogate-based Optimization (Sbo)mentioning

confidence: 99%

Surrogate-based infill optimization applied to electromagnetic problems

Couckuyt

Declercq

Dhaene

et al. 2010

Int J RF and Microwave Comp Aid Eng

125

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The increasing use of expensive computer simulations in engineering places a serious computational burden on associated optimization problems. Surrogate-based optimization becomes standard practice in analyzing such expensive black-box problems. This article discusses several approaches that use surrogate models for optimization and highlights one sequential design approach in particular, namely, expected improvement. The expected improvement approach is demonstrated on two electromagnetic problems, namely, a microwave filter and a textile antenna.

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“…In recent years, several studies have proposed EAs incorporating fitness approximation with an aim to improve performance while not incurring expensive computational cost [15,23,28]. Typically these EAs employ a surrogate model in place of the expensive original function evaluations.…”

Section: Fitness Approximationmentioning

confidence: 99%

Improving Local Convergence in Particle Swarms by Fitness Approximation Using Regression

Bird

2010

Computational Intelligence in Expensive Optimization Problems

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Abstract. In this chapter we present a technique that helps Particle Swarm Optimisers (PSOs) locate an optimum more quickly, through fitness approximation using regression. A least-squares regression is used to estimate the shape of the local fitness landscape. From this shape, the expected location of the peak is calculated and the information given to the PSO. By guiding the PSO to the optimum, the local convergence speed can be vastly improved. We demonstrate the effectiveness of using regression on several static multimodal test functions as well as dynamic multimodal test scenarios (Moving Peaks). This chapter also extends the Moving Peaks test suite by enhancing the standard conic peak function to allow the creation of asymmetrical and additional multiple local peaks. The combination of this technique and a speciation-based PSO compares favourably to another multi-swarm PSO algorithm that has proven to be working well on the Moving peaks test functions.

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Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling

Cited by 497 publications

References 28 publications

Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO

Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO

Surrogate-based infill optimization applied to electromagnetic problems

Improving Local Convergence in Particle Swarms by Fitness Approximation Using Regression

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