Structural optimization using evolution strategies and neural networks

Papadrakakis, Manolis; Lagaros, Nikos D.; Tsompanakis, Yiannis

doi:10.1016/s0045-7825(97)00215-6

Cited by 183 publications

(88 citation statements)

References 14 publications

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“…In order to alleviate any inaccuracies entailed by the NN-based structural analysis a correction on the output values is proposed [32], especially when the constraint value is near the limit which divides the feasible with the infeasible region. Thus a relaxation of this limit is introduced before entering the optimization procedure during the NN testing phase.…”

Section: Deterministic-based Structural Optimization Using Es and Nnmentioning

confidence: 99%

Soft computing methodologies for structural optimization

Papadrakakis

Lagaros

2003

Applied Soft Computing

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The paper examines the efficiency of soft computing techniques in structural optimization, in particular algorithms based on evolution strategies combined with neural networks, for solving large-scale, continuous or discrete structural optimization problems. The proposed combined algorithms are implemented both in deterministic and reliability based structural optimization problems, in an effort to increase the computational efficiency as well as the robustness of the optimization procedure. The use of neural networks was motivated by the time-consuming repeated finite element analyses required during the optimization process. A trained neural network is used to perform either the deterministic constraints check or, in the case of reliability based optimization, both the deterministic and the probabilistic constraints checks. The suitability of the neural network predictions is investigated in a number of structural optimization problems in order to demonstrate the computational advantages of the proposed methodologies.

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Section: Deterministic-based Structural Optimization Using Es and Nnmentioning

confidence: 99%

Soft computing methodologies for structural optimization

Papadrakakis

Lagaros

2003

Applied Soft Computing

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“…In the past few years, many swarm intelligence algorithms have also been proposed to solve practical optimization issues, such as particle swarm optimization (PSO) [44], genetic algorithm (GA) [45], ant colony optimization (ACO) [46], differential evolution algorithm (DE) [47], evolutionary strategy (ES) [48], evolutionary programming (EP) [49,50] and fruit fly optimization algorithm (FOA) [51][52][53]. Moth-flame optimization (MFO) is a novel nature-enlightenment algorithm, which was proposed by Mirjalili in 2015 to compete with the current optimization algorithms [54].…”

Section: Introductionmentioning

confidence: 99%

Using GM (1,1) Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia

Zhao

Guo

2016

Applied Sciences

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Accurate and reliable forecasting on annual electricity consumption will be valuable for social projectors and power grid operators. With the acceleration of electricity market reformation and the development of smart grid and the energy Internet, the modern electric power system is becoming increasingly complex in terms of structure and function. Therefore, electricity consumption forecasting has become a more difficult and challenging task. In this paper, a new hybrid electricity consumption forecasting method, namely grey model (1,1) (GM (1,1)), optimized by moth-flame optimization (MFO) algorithm with rolling mechanism (Rolling-MFO-GM (1,1)), was put forward. The parameters a and b of GM (1,1) were optimized by employing moth-flame optimization algorithm (MFO), which is the latest natured-inspired meta-heuristic algorithm proposed in 2015. Furthermore, the rolling mechanism was also introduced to improve the precision of prediction. The Inner Mongolia case discussion shows the superiority of proposed Rolling-MFO-GM (1,1) for annual electricity consumption prediction when compared with least square regression (LSR), GM (1,1), FOA (fruit fly optimization)-GM (1,1), MFO-GM (1,1), Rolling-LSR, Rolling-GM (1,1) and Rolling-FOA-GM (1,1). The grey forecasting model optimized by MFO with rolling mechanism can improve the forecasting performance of annual electricity consumption significantly.

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“…One of the first surrogate-assisted (µ + λ)-ES and (µ, λ)-ES were proposed in [23], where evaluations of an expensive structural optimization problem were replaced by a hiddenlayer Artificial Neural Network (ANN) trained by back-propagation. The authors suggested to re-learn the model at each iteration by adding new training points randomly drawn from a Gaussian distribution with the mean located in the center of the decision space.…”

Section: Introductionmentioning

confidence: 99%

Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es)

Loshchilov

Schoenauer

Sebag

2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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This paper presents a new mechanism for a better exploitation of surrogate models in the framework of Evolution Strategies (ESs). This mechanism is instantiated here on the self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution Strategy ( s * ACM-ES), a recently proposed surrogate-assisted variant of CMA-ES. As well as in the original s * ACM-ES, the expensive function is optimized by exploiting the surrogate model, whose hyper-parameters are also optimized online. The main novelty concerns a more intensive exploitation of the surrogate model by using much larger population sizes for its optimization.The new variant of s * ACM-ES significantly improves the original s *ACM-ES and further increases the speed-up compared to the CMA-ES, especially on unimodal functions (e.g., on 20-dimensional Rotated Ellipsoid, s * ACM-ES is 6 times faster than aCMA-ES and almost by one order of magnitude faster than CMA-ES). The empirical validation on the BBOB-2013 noiseless testbed demonstrates the efficiency and the robustness of the proposed mechanism.

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Structural optimization using evolution strategies and neural networks

Cited by 183 publications

References 14 publications

Soft computing methodologies for structural optimization

Soft computing methodologies for structural optimization

Using GM (1,1) Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia

Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es)

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