A multi-objective metamodel-assisted memetic algorithm with strength-based local refinement

Georgopoulou, Chara; Giannakoglou, Kyriakos C.

doi:10.1080/03052150902866577

Cited by 23 publications

(10 citation statements)

References 28 publications

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“…The major advantage of EAs is that they do not get trapped into local minima. In this paper, EAs are exclusively used although the authors' group has experience on both methods [31,32] or their hybridization, including memetic algorithms [10].…”

Section: Applicationsmentioning

confidence: 99%

A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization

Asouti

Kampolis

Giannakoglou

2009

Genet Program Evolvable Mach

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A Grid-enabled asynchronous metamodel-assisted evolutionary algorithm is presented and assessed on a number of aerodynamic shape optimization problems. An efficient way of implementing surrogate evaluation models or metamodels (artificial neural networks) in the context of an asynchronous evolutionary algorithm is proposed. The use of metamodels relies on the inexact pre-evaluation technique already successfully applied to synchronous (i.e. generation-based) evolutionary algorithms, which needs to be revisited so as to efficiently cooperate with the asynchronous search method. The so-created asynchronous metamodel-assisted evolutionary algorithm is further enabled for Grid Computing. The Grid deployment of the algorithm relies on three middleware layers: GridWay, Globus Toolkit and Condor. Single-and multi-objective CFD-based designs of isolated airfoils and compressor cascades are handled using the proposed algorithm and the gain in CPU cost is demonstrated.

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Section: Applicationsmentioning

confidence: 99%

A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization

Asouti

Kampolis

Giannakoglou

2009

Genet Program Evolvable Mach

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“…Tenne and Armfield [48] suggested a memetic optimization framework using variable global and local surrogate-models for optimization of expensive functions. Also within a framework of memetic algorithms, Georgopoulou and Giannakoglou [49] proposed to perform a low-cost pre-evaluation of candidate solutions using RBF networks in global search and the gradientbased refinement of promising solutions during the local search. In [50], a global surrogate model was proposed for better pre-offspring selection, and a local surrogate model was used to approximate the fitness in local search.…”

Section: ) Local-surrogate Assisted Metaheuristic Algorithmsmentioning

confidence: 99%

Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

Sun

Jin

Cheng

et al. 2017

IEEE Trans. Evol. Computat.

327

131

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Abstract-Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization algorithm and a surrogate-assisted social learning based particle swarm optimization algorithm cooperatively search for the global optimum. The cooperation between the particle swarm optimization and the social learning based particle swarm optimization consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the social learning based particle swarm optimization focuses on exploration while the particle swarm optimization concentrates on local search. Empirical studies on six 50-dimensional and six 100-dimensional benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.

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“…An added purpose of the investigation is to be able to solve MO problems where the number of decision variables varies between 15 and 25. To this effect we propose a new algorithm, GOMORS, that combines radial basis function approximation with multi-objective evolutionary optimization, within the general iterative framework of surrogate-assisted heuristic search algorithms. Our approach is different from prevalent RBF based MO algorithms that use evolutionary algorithms [13,20,25,45,51]. Most RBF based evolutionary algorithms employ surrogates in an inexact pre-evaluation phase (IPE) in order to inexpensively evaluate child populations after every MOEA generation.…”

Section: Introductionmentioning

confidence: 99%

“…Ponweiser et al [33] and Beume et al [2] explored the idea of maximizing expected improvement in hypervolume. Authors have also explored the use of other function approximation techniques inside an optimization algorithm, including Radial Basis Functions (RBFs) [13,20,25,45,51], Support Vector Machines [24,43] and Artificial Neural Networks [7]. Evolutionary algorithms are the dominant optimization algorithms used in these methods.…”

Section: Introductionmentioning

confidence: 99%

“…They also recommend the use of locally fitted RBFs for surrogate approximation. Georgopoulou and Giannakoglou [13] build upon [20] by employing gradient based refinements of promising solutions highlighted by RBF approximation during each MOEA generation. Santana et al [45] divide the heuristic search into two phases where the first phase employs a global surrogate-assisted search within an MOEA framework, and rough set theory is used in the second phase for local refinements.…”

Section: Introductionmentioning

confidence: 99%

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Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection

2015

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GOMORS is a parallel response surface-assisted evolutionary algorithm approach to multi-objective optimization that is designed to obtain good non-dominated solutions to black box problems with relatively few objective function evaluations. GOMORS uses Radial Basic Functions to iteratively compute surrogate response surfaces as an approximation of the computationally expensive objective function. A multi objective search utilizing evolution, local search, multi method search and non-dominated sorting is done on the surrogate radial basis function surface because it is inexpensive to compute. A balance between exploration, exploitation and diversification is obtained through a novel procedure that simultaneously selects evaluation points within an algorithm iteration through different metrics including Approximate Hypervolume Improvement, Maximizing minimum domain distance, Maximizing minimum objective space distance, and surrogate-assisted local search, which can be computed in parallel. The results are compared to ParEGO (a kriging surrogate method solving many weighted single objective optimizations) and the widely used NSGA-II. The results indicate that GOMORS outperforms ParEGO and NSGA-II on problems tested. For example, on a groundwater PDE problem, GOMORS outperforms ParEGO with 100, 200 and 400 evaluations for a 6 dimensional problem, a 12 dimensional problem and a 24 dimensional problem. For a fixed number of evaluations, the differences in performance between GOMORS and ParEGO become larger as the number of dimensions increase. As the number of evaluations increase, the differences between GOMORS and ParEGO become smaller. Both surrogate-based methods are much better than NSGA-II for all cases considered.Electronic supplementary material The online version of this article

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A multi-objective metamodel-assisted memetic algorithm with strength-based local refinement

Cited by 23 publications

References 28 publications

A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization

A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization

Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems

Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection

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