A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems

Wang, Nianfeng F.; Zhang, Xianmin; Yang, Yue

doi:10.1016/j.asoc.2013.03.013

Cited by 19 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A majority thereof addresses various formulations of compliance objective functions subject to weight constraints or vice versa [2, 22, 23, 34, 42, 51, 94, 103, 110-112, 115, 129, 138, 152, 153, 155, 183, 187, 196, 205, 211], sometimes also formulated by means of displacement constraints [72,73,205]. Some authors have addressed compliant mechanisms [94, 148-153, 187, 199], inner stress [182], eigenvalue [115], stress and displacements under uncertainty [200] or target matching [186,198,201,203] problems. Although the analytical sensitivities are known for these problems, they can serve as important references to evaluate the performance of an evolutionary optimization approach.…”

Section: Discussionmentioning

confidence: 99%

“…Several extensions have been proposed [186,198,200], for instance distinguishing between active and inactive curves that can change their state during the optimization [198], so that only active curves contribute to the structure. Furthermore, a hybrid genetic algorithm with local search for a multi-objective optimization has been proposed recently [201]. By a different research group [160,163], a structure is defined more simply by connected piece-wise linear segments with different length and orientations.…”

Section: Geometric Representationmentioning

confidence: 99%

See 1 more Smart Citation

Generic Topology Optimization Based on Local State Features

Aulig¹

2017

View full text Add to dashboard Cite

The work at hand addresses engineers, designers and scientists who face the challenging Task of devising concept structures in a virtual product design process that involves more and more sophisticated physical simulations. Using methods of evolutionary optimization and machine learning, this dissertation explores a novel generic topology optimization algorithm, which is able to provide concept designs even for problems involving complex, black-box simulations. A self-contained learning component utilizes physical simulation data to generate a search direction. The generic topology optimization is studied in conjunction with statistical models such as neural networks or support vector regression. In empirical experiments, the novel method reproduces reference structures with Minimum compliance and provides innovative solutions in the domain of vehicle crashworthiness optimization. Contents Symbols and Abbreviations XIV Abstract XVII Zusammenfassung XVIII 1 Introduction 1 2 Fundamentals...

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Geometric Representationmentioning

confidence: 99%

Generic Topology Optimization Based on Local State Features

Aulig¹

2017

View full text Add to dashboard Cite

show abstract

“…Knowing that it is impossible to find the whole PS of continuous MOPs, we aim at finding a finite set of Pareto optimal the ant colony based algorithms [18], the immune based algorithms [19][20][21] and the estimation of distribution algorithm [22,23]. Moreover, MOEAs for complicated MOPs have also been extensively investigated which include MOEA for dynamic MOPs [24], constraint MOPs [25] and many objective optimization problems [26].…”

Section: Introductionmentioning

confidence: 99%

An immune multi-objective optimization algorithm with differential evolution inspired recombination

Hou

Yin

et al. 2015

Applied Soft Computing

View full text Add to dashboard Cite

“…• Both methods have been successfully applied separately and many papers have been published focused on them (Wang et al, 2013;Zhao et al, 2013;Busoniu et al, 2011). However, as far as we know, the hybridization between these two methods has not been done before.…”

Section: Introductionmentioning

confidence: 99%

GACE: A meta-heuristic based in the hybridization of Genetic Algorithms and Cross Entropy methods for continuous optimization

Onieva

Osaba

Masegosa

et al. 2016

Expert Systems with Applications

View full text Add to dashboard Cite

Metaheuristics have proven to get a good performance solving difficult optimization problems in practice. Despite its success, metaheuristics still suffers from several problems that remains open as the variability of their performance depending on the problem or instance being solved. One of the approaches to deal with these problems is the hybridization of techniques. This paper presents a hybrid metaheuristic that combines a Genetic Algorithm (GA) with a Cross Entropy (CE) method to solve continuous optimization functions. The algorithm divides the population into two sub-populations, in order to apply GA in one sub-population and CE in the other. The proposed method is tested on 24 continuous benchmark functions, with four different dimension configurations. First, a study to find the best parameter configuration is done. The best configuration found is compared with several algorithms in the literature in order to demonstrate the competitiveness of the proposal. The results shows that GACE is the best performing method for instances with high dimensionality. Statistical tests have been applied, to support the conclusions obtained in the experimentation.

show abstract

A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems

Cited by 19 publications

References 22 publications

Generic Topology Optimization Based on Local State Features

Generic Topology Optimization Based on Local State Features

An immune multi-objective optimization algorithm with differential evolution inspired recombination

GACE: A meta-heuristic based in the hybridization of Genetic Algorithms and Cross Entropy methods for continuous optimization

Contact Info

Product

Resources

About