Generalized extremal optimization: An application in heat pipe design

Sousa, Fabiano Luís de; Vlassov, Valeri V.; Ramos, Fernando M.

doi:10.1016/j.apm.2004.04.004

Cited by 56 publications

(23 citation statements)

References 12 publications

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“…To make the EO method applicable to a broad class of design optimization problems, the generalized extremal optimization (GEO) was proposed by Sousa et al [18]. It is developed to operate on bit strings, capable of tackle continuous, discrete and/or integer variables without concern to how the fitness of the design variables is assigned.…”

Section: Generalized Extremal Optimizationmentioning

confidence: 99%

“…To enhance EO, other extended EO methods are also presented with the proceed of research. In order to make fitness define more easily a generalization of the EO method was devised and implemented by De Sousa et al [18]. In that new algorithm, which is named generalized extremal optimization (GEO), the fitness is assigned to a population of species of bits and the binary bit to mutate is chosen according to probability distribution over the ranked bit order.…”

Section: Introductionmentioning

confidence: 99%

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The computing of the optimal power consumption for semi-track air-cushion vehicle using hybrid generalized extremal optimization

Xie

Liu

2009

Applied Mathematical Modelling

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a b s t r a c tA new stochastic method named hybrid generalized extremal optimization (HGEO) is proposed in this paper. It combines genetic algorithms (GAs) and generalized extremal optimization (GEO). In order to extend GEO's mutation operator to accelerate convergence speed and be easily incorporated into HGEO, the real coded GEO is first developed to populationbase GEO (PGEO), and then incorporated into the HGEO in the paper. Constraints consideration for using the HGEO and the effects of related operators are also investigated. Finally, the performance of the HGEO is fully investigated compared with other related algorithms to find the optimal power consumption for the semi-track air-cushion vehicle (STACV). The results show that the HGEO has better performance than GAs or other related simpler algorithms.

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Section: Generalized Extremal Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The computing of the optimal power consumption for semi-track air-cushion vehicle using hybrid generalized extremal optimization

Xie

Liu

2009

Applied Mathematical Modelling

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“…A detailed explanation of GEO can be found in [25,26], while examples of applications to real complex design problems can be found in [10,[25][26][27].…”

Section: D03mentioning

confidence: 99%

“…This algorithm was developed to be easily applicable to a broad class of nonlinear constrained optimization problems, with the presence of any combination of continuous, discrete and integer values, while having only one free parameter to be adjusted. Its efficacy to tackle complex design spaces has been demonstrated with test functions and real design problems [10,[24][25][26][27]. Nonetheless, being a new algorithm, many of its features remain to be explored, such as parallelization, hybridization with other optimization algorithms, or different types of representation for the design variables.…”

Section: Introductionmentioning

confidence: 99%

Application of the generalized extremal optimization algorithm to an inverse radiative transfer problem

Sousa

Soeiro

Neto

et al. 2007

Inverse Problems in Science and Engineering

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-The recently developed Generalized Extremal Optimization (GEO) algorithm is applied for the solution of an inverse problem of radiative properties estimation. A comparison with two other stochastic methods, Simulated Annealing (SA) and Genetic Algorithms (GAs), is also performed, demonstrating that GEO is competitive. From the test case results we could also infer that a hybridization of GEO with gradient based methods is very promising.

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“…GEO algorithm is a global search meta-heuristic that generalizes the extremal optimization (EO) method [4] inspired by a model of natural evolution. GEO is specially devised to be used in constrained or unconstrained problems, non-convex or even disjoint design spaces, with any combination of continuous, discrete or integer variables [8,9].…”

Section: Solution Representationmentioning

confidence: 99%

A multi-objective evolutionary algorithm to obtain test cases with variable lengths

Yano

Martins

Sousa

2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

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In this paper a new multi-objective implementation of the generalized extremal optimization (GEO) algorithm, named M-GEO vsl , is presented. It was developed primarily to be used as a test case generator to find transition paths from extended finite state machines (EFSM), taking into account not only the transition to be covered but also the minimization of the test length. M-GEO vsl has the capability to deal with strings whose number of elements vary dynamically, making it possible to generate solutions with different lengths. The steps of the algorithm are described for a general multi-objective problem in which the solution length is an element to be optimized. Experiments were performed to generate test case from EFSM benchmark models using M-GEO vsl and the approach was compared with a related work. Categories and Subject Descriptors

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Generalized extremal optimization: An application in heat pipe design

Cited by 56 publications

References 12 publications

The computing of the optimal power consumption for semi-track air-cushion vehicle using hybrid generalized extremal optimization

The computing of the optimal power consumption for semi-track air-cushion vehicle using hybrid generalized extremal optimization

Application of the generalized extremal optimization algorithm to an inverse radiative transfer problem

A multi-objective evolutionary algorithm to obtain test cases with variable lengths

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