General variable neighborhood search for the continuous optimization

Mladenović, Nenad; Dražić, Milan; Kovačevíc-Vujčić, Vera; Čangalović, Mirjana

doi:10.1016/j.ejor.2006.12.064

Cited by 119 publications

(64 citation statements)

References 13 publications

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“…It has been mainly applied to combinatorial optimization problems, see e.g. [25,40], or continuous problems with a combinatorial structure [6,5,25], though it has also been recently proposed for continuous optimization problems, [8,16,17,33,42,40]. In [10], a basic version of VNS for SVM parameter tuning can be found, together with the task of gene selection.…”

Section: Preliminariesmentioning

confidence: 99%

A nested heuristic for parameter tuning in Support Vector Machines

Carrizosa¹,

Martín-Barragán²,

Morales³

2014

Computers & Operations Research

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The default approach for tuning the parameters of a Support Vector Machine (SVM) is a grid search in the parameter space. Different metaheuristics have been recently proposed as a more efficient alternative, but they have only shown to be useful in models with a low number of parameters. Complex models, involving many parameters, can be seen as extensions of simpler and easy-to-tune models, yielding a nested sequence of models of increasing complexity. In this paper we propose an algorithm which successfully exploits this nested property, with two main advantages versus the state of the art. First, our framework is general enough to allow one to address, with the very same method, several popular SVM parameter models encountered in the literature. Second, as algorithmic requirements we only need either an SVM library or any routine for the minimization of convex quadratic functions under linear constraints. In the computational study, we address Multiple Kernel Learning tuning problems for which grid search clearly would be infeasible, while our classification accuracy is comparable to that of ad-hoc modeldependent benchmark tuning methods.

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Section: Preliminariesmentioning

confidence: 99%

A nested heuristic for parameter tuning in Support Vector Machines

Carrizosa¹,

Martín-Barragán²,

Morales³

2014

Computers & Operations Research

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“…Therefore, for the shaking step, Mladenović et al 22 consider several basic VNS heuristics, each using different metric function in designing neighborhoods. Users have full freedom to choose any combination of those heuristics.…”

Section: Continuous General Variable Neighborhood Search (Cgvns) Metamentioning

confidence: 99%

“…Mladenović el al. 57 and Kovacevic-Vujcić et al 58 develop the software package global optimization for general box-constrained nonlinear programs. For the local search phase of VNS, several non-linear programming tools and methods, such as steepest descent, Rosenbrock, Nelder-Mead, FletcherReeves, are included in our study.…”

Section: Continuous General Variable Neighborhood Search (Cgvns) Metamentioning

confidence: 99%

“…Continuous general variable neighborhood search (CGVNS) introduced by Mladenović et al 22 is a topnotch methodology capable of solving different types of continuous optimization which has been introduced in the recent years. The notable advantage of CGVNS as opposed to most local search-based heuristics is the utilization of solely one neighborhood search structure in that it systematically changes pre-specified neighborhoods within a local search strategy and owns fewer parameters to adjust.…”

Section: Introductionmentioning

confidence: 99%

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A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study

Vahdani¹,

Mousavi²,

Tavakkoli–Moghaddam³

et al. 2017

IJCIS

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In sustainable supply chain networks, companies are obligated to have a systematic decision support system in place to help it adopt right decisions at right times. Among strategic decisions, supplier selection and evaluation outranks other decisions in terms of importance due to its long-term impacts. Besides, the adoption of such strategic decision entails exploring several factors that contribute to the complexity of decision making in the supply chain. For the purpose of solving non-linear regression problems, a novel neural network technique known as least squaresupport vector machine (LS-SVM) with maximum generalization ability has successfully been implemented. However, the performance quality of the LS-SVM is recognized to notoriously vary depending on the rigorous selection of its parameters. Therefore, in this paper, a continuous general variable neighborhood search (CGVNS) which is an effective meta-heuristic algorithm to solve the real world engineering continuous optimization problems is proposed to be integrated with LS-SVM. The CGVNS is hybridized in our novel integrated LS-SVM and CGVNS model, to tune the parameters of the LS-SVM to better estimate performance rating of supplier selection and evaluation problem. To demonstrate the improved performance of our proposed integrated model, a real data set from a case study of a supplier selection and evaluation problem is presented in a cosmetics industry. Additionally, comparative evaluations between our proposed model and the conventional techniques, namely nonlinear regression, multi-layer perceptron (MLP) neural network and LS-SVM is provided. The experimental results simply manifest the outperformance of our proposed model in terms of estimation accuracy and effective prediction.

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“…Many promising methods have been proposed to solve the mentioned problem in Equation 1, for example, genetic algorithms [14,24], particle swarm optimization [23,28], ant colony optimization [31], tabu search [9,10], differential evolution [2,6] scatter search [15,20], and variable neighborhood search [11,27]. Although the efficiency of these methods, when applied to lower and middle dimensional problems, e.g., D < 100, many of them suffer from the curse of dimensionality when applied to high dimensional problems.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Simulated Annealing and Nelder-Mead Algorithm for Solving Large-Scale Global Optimization Problems

Ali

2014

IJORCS

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General variable neighborhood search for the continuous optimization

Cited by 119 publications

References 13 publications

A nested heuristic for parameter tuning in Support Vector Machines

A nested heuristic for parameter tuning in Support Vector Machines

A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study

Hybrid Simulated Annealing and Nelder-Mead Algorithm for Solving Large-Scale Global Optimization Problems

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