• Structural Engineering and Mechanics
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• v.44 no.3
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• pp.379-403
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• 2012

In recent years, the need for optimal design of structures under time-history loading aroused great attention in researchers. The main problem in this field is the extremely high computational demand of time-history analyses, which may convert the solution algorithm to an illogical one. In this paper, a new framework is developed to solve the size optimization problem of steel truss structures subjected to ground motions. In order to solve this problem, the covariance matrix adaptation evolution strategy algorithm is employed for the optimization procedure, while a generalized regression neural network is utilized as a meta-model for fitness approximation. Moreover, the computational cost of time-history analysis is decreased through a wavelet analysis. Capability and efficiency of the proposed framework is investigated via two design examples, comprising of a tower truss and a footbridge truss.

• Advances in Computational Design
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• v.1 no.4
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• pp.315-327
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• 2016

Damage detection and localisation in structures is essential since it can be a means for preventive maintenance of those structures under service conditions. The use of structural modal data for detecting the damage is one of the most efficient methods. This paper presents comparative performance of various state-of-the-art meta-heuristics for use in structural damage detection based on changes in modal data. The metaheuristics include differential evolution (DE), artificial bee colony algorithm (ABC), real-code ant colony optimisation (ACOR), charged system search (ChSS), league championship algorithm (LCA), simulated annealing (SA), particle swarm optimisation (PSO), evolution strategies (ES), teaching-learning-based optimisation (TLBO), adaptive differential evolution (JADE), evolution strategy with covariance matrix adaptation (CMAES), success-history based adaptive differential evolution (SHADE) and SHADE with linear population size reduction (L-SHADE). Three truss structures are used to pose several test problems for structural damage detection. The meta-heuristics are then used to solve the test problems treated as optimisation problems. Comparative performance is carried out where the statistically best algorithms are identified.

• Economic and Environmental Geology
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• v.53 no.6
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• pp.703-713
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• 2020

In many geological fields, including hydrogeology, it is of great importance to determine the heterogeneity of the subsurface media. This study briefly introduces the concept and theory of the method that can estimate the hydraulic properties of the media constituting the aquifer, which was recently introduced by Park (2020). After the introduction, the method was applied to the synthetic aquifer to demonstrate the practicality, from which various implications were drawn. The introduced technique uses a global optimization technique called the covariance matrix adaptation evolution strategy (CMA-ES). Conceptually, it is a methodology to characterize the aquifer heterogeneity by assimilating the groundwater level time-series data due to the imposed hydraulic stress. As a result of applying the developed technique to estimate the hydraulic conductivity of a hypothetical aquifer, it was confirmed that a total of 40000 unknown values were estimated in an affordable computational time. In addition, the results of the estimates showed a close numerical and structural similarity to the reference hydraulic conductivity field, confirming that the quality of the estimation by the proposed method is high. In this study, the developed method was applied to a limited case, but it is expected that it can be applied to a wider variety of cases through additional development of the method. The development technique has the potential to be applied not only to the field of hydrogeology, but also to various fields of geology and geophysics. Further development of the method is currently underway.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.263-277
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• 2024

Artificial Neural Networks (ANNs) are artificial learning algorithms that provide successful results in solving many machine learning problems such as classification, prediction, object detection, object segmentation, image and video classification. There is an increasing number of studies that use ANNs as a prediction tool in soil classification. The aim of this research was to understand the role of hyperparameter optimization in enhancing the accuracy of ANNs for soil type classification. The research results has shown that the hyperparameter optimization and hyperparamter optimized ANNs can be utilized as an efficient mechanism for increasing the estimation accuracy for this problem. It is observed that the developed hyperparameter tool (HyperNetExplorer) that is utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES), Genetic Algorithm (GA) and Jaya Algorithm (JA) optimization techniques can be successfully used for the discovery of hyperparameter optimized ANNs, which can accomplish soil classification with 100% accuracy.

• Korean Journal of Optics and Photonics
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• v.35 no.3
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• pp.107-114
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• 2024

In this study, the feasibility of using the covariance-matrix-adaptation-evolution-strategy (CMA-ES) algorithm in a multichannel coherent-beam-combining (CBC) system was experimentally verified. We constructed a multichannel CBC system utilizing a spatial light modulator (SLM) as a multichannel phase-modulator array, along with a coherent light source at 635 nm, implemented the stochastic-parallel-gradient-descent (SPGD) and CMA-ES algorithms on it, and compared their performances. In particular, we evaluated the characteristics of the CMA-ES and SPGD algorithms in the CBC system in both 16-channel rectangular and 19-channel honeycomb formats. The results of the evaluation showed that the performances of the two algorithms were similar on average, under the given conditions; However, it was verified that under the given conditions the CMA-ES algorithm was able to operate with more stable performance than the SPGD algorithm, as the former had less operational variation with the initial phase setting than the latter. It is emphasized that this study is the first proof-of-principle demonstration of the CMA-ES phase-control algorithm in a multichannel CBC system, to the best of our knowledge, and is expected to be useful for future experimental studies of the effects of additional channel-number increments, or external-phase-noise effects, in multichannel CBC systems based on the CMA-ES phase-control algorithm.

• Korean Chemical Engineering Research
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• v.52 no.4
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• pp.467-474
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• 2014

Preliminary design in chemical process furnishes economic feasibility through calculation of both mass balance and energy balance and makes it possible to produce a desired product under the given conditions. Through this design stage, the process possesses unchangeable characteristics, since the materials, reactions, unit configuration, and operating conditions were determined. Unique characteristics could be very economic, but it also implies various potential risk factors as well. Therefore, it becomes extremely important to design process considering both economics and safety by integrating process simulation and quantitative risk analysis during preliminary design stage. The target of this study is LNG liquefaction process. By the simulation using Aspen HYSYS and quantitative risk analysis, the design variables of the process were determined in the way to minimize the inherent explosion risks and operating cost. Instead of the optimization tool of Aspen HYSYS, the optimization was performed by using stochastic optimization algorithm (Covariance Matrix Adaptation-Evolution Strategy, CMA-ES) which was implemented through automation between Aspen HYSYS and Matlab. The research obtained that the important variable to enhance inherent safety was the operation pressure of mixed refrigerant. The inherent risk was able to be reduced about 4~18% by increasing the operating cost about 0.5~10%. As the operating cost increases, the absolute value of risk was decreased as expected, but cost-effectiveness of risk reduction had decreased. Integration of process simulation and quantitative risk analysis made it possible to design inherently safe process, and it is expected to be useful in designing the less risky process since risk factors in the process can be numerically monitored during preliminary process design stage.