• Structural Engineering and Mechanics
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• v.69 no.1
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• pp.21-31
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• 2019

Optimizing composite structures to exploit their maximum potential is a realistic application with promising returns. In this research, simultaneous maximization of the fundamental frequency and frequency separation between the first two modes by optimizing the fiber angles is considered. A high-fidelity design optimization methodology is developed by combining the high-accuracy of finite element method with iterative improvement capability of metaheuristic algorithms. Three powerful nature-inspired optimization algorithms viz. a genetic algorithm (GA), a particle swarm optimization (PSO) variant and a cuckoo search (CS) variant are used. Advanced memetic features are incorporated in the PSO and CS to form their respective variants-RPSOLC (repulsive particle swarm optimization with local search and chaotic perturbation) and CHP (co-evolutionary host-parasite). A comprehensive set of benchmark solutions on several new problems are reported. Statistical tests and comprehensive assessment of the predicted results show CHP comprehensively outperforms RPSOLC and GA, while RPSOLC has a little superiority over GA. Extensive simulations show that the on repeated trials of the same experiment, CHP has very low variability. About 50% fewer variations are seen in RPSOLC as compared to GA on repeated trials.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.243-251
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• 2013

Cash management is a major concern for all contractors in the construction industry. It is arguable that cash is the most critical resource of all. A contractor needs to secure sufficient funds to navigate the project to the end, while keeping an eye on maximizing profits along the way. Past research attempted to address such topic via developing models to tackle the time-cost tradeoff problem, cash flow forecasting, and cash flow management. Yet, little was done to integrate the three aspects of cash management together. This paper, as such, presents a comprehensive model that integrates the time-cost tradeoff problem, cash flow management, and cash flow forecasting. First, the model determines the project optimal completion time by considering the different alternative construction methods available for executing project activities. Second, it investigates different funding alternatives and proposes a project-level cash management plan. Two funding alternatives are considered; they are borrowing and company own financing. The model was built as a combinatorial optimization model that utilizes ant colony search capabilities. The model also utilizes Microsoft Project software and spreadsheets to maintain an environment that incorporates activities, their durations, and other project data, in order to estimate project completion time and cost. Ant Colony Optimization algorithm was coded as a Macro program using VBA. Finally, an example project was used to test the developed model, where it acted reliably in maximizing the contractor's profit in the test project.

• Journal of the Ergonomics Society of Korea
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• v.31 no.6
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• pp.733-740
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• 2012

Objective: The present study systematically analyzed the characteristics of ergonomic layout optimization methods by a comprehensive literature survey. Background: Although layout design methods for ergonomic placement of controls and displays on a console have been developed, understanding of their characteristics is lacking. Method: The present study analyzed layout optimization papers published past 20 years from the following four aspects: optimization model, optimization algorithm, design principle, and constraint/assumption. Results: The existing layout optimization methods based on various optimization techniques consider only a partial set of four layout principles(importance, frequency of use, sequence of use, and functional grouping) and two ergonomic criteria(visibility and reach). In addition, the existing methods oversimplify components in various sizes, shapes, and angles by assuming the equality of the components in size and shape. Conclusion: A more effective layout optimization method is needed which considers the layout principles and ergonomic criteria in a comprehensive manner and reflect the diversity of components in size and shape. Application: The identified characteristics on the existing layout optimization methods can be applicable to development of a better ergonomic console layout design method.

• Journal of Power Electronics
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• v.3 no.2
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• pp.69-80
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• 2003

Computer aided engineering (CAE) is a systematic approach to develop a better product/application with maximum possible options and minimum transition time. This paper presents a comprehensive feasibility analysis of various CAE techniques for evaluation and optimization of power electronic converters and systems. Different CAE methods for analysis, design, and performance improvement are classified. In addition, their advantages compared to the conventional workbench experimental methods are explained in detail and through examples.

• Geomechanics and Engineering
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• v.31 no.1
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• pp.113-127
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• 2022

The undrained shear strength of soil is considered one of the engineering parameters of utmost significance in geotechnical design methods. In-situ experiments like cone penetration tests (CPT) have been used in the last several years to estimate the undrained shear strength depending on the characteristics of the soil. Nevertheless, the majority of these techniques rely on correlation presumptions, which may lead to uneven accuracy. This research's general aim is to extend a new united soft computing model, which is a combination of random forest (RF) with grasshopper optimization algorithm (GOA) to the pile set-up parameters' better approximation from CPT, based on two different types of data as inputs. Data type 1 contains pile parameters, and data type 2 consists of soil properties. The contribution of this article is that hybrid GOA - RF for the first time, was suggested to forecast the pile set-up parameter from CPT. In order to do this, CPT data and related bore log data were gathered from 70 various locations across Louisiana. With an R² greater than 0.9098, which denotes the permissible relationship between measured and anticipated values, the results demonstrated that both models perform well in forecasting the set-up parameter. It is comprehensible that, in the training and testing step, the model with data type 2 has finer capability than the model using data type 1, with R² and RMSE are 0.9272 and 0.0305 for the training step and 0.9182 and 0.0415 for the testing step. All in all, the models' results depict that the A parameter could be forecasted with adequate precision from the CPT data with the usage of hybrid GOA - RF models. However, the RF model with soil features as input parameters results in a finer commentary of pile set-up parameters.

• Journal of Korean Society of Water and Wastewater
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• v.23 no.4
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• pp.417-430
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• 2009

A BNR comprehensive performance evaluation (BNR CPE) system was established employing system-oriented evaluation methodology for biological nutrient removal (BNR) processes based on the CPE techniques developed by U.S. EPA for evaluation of conventional biological processes. The BNR CPE system applied to five domestic BNR plants adopting $A^2/O$ process confirmed that all target plants except the smallest one had not any serious defective performance and process stability was enhanced with increasing plant size. The system also clearly verified relatively poor performances in anoxic reactors without exception mainly due to influent carbon limit rather than functional defect. Consistent good performances were confirmed even during both winter season and wet weather generally known to be difficult to achieve satisfactory removals. Presentation of evaluation results by modified radar chart system simplified and clarified the evaluation and analysis procedures. The BNR CPE system could not only discover readily the causes of present and prospective poor performances but also facilitate the suggestion of their optimization options. Mutual effect and cause-and-effect among operation parameters and unit processes were also found easily using the evaluation system. The system justified that the adverse effect of defective operating parameters could be compensated by other favorable parameters, especially in anaerobic and anoxic reactors as well as during the winter season.

• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.1008-1012
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• 2020

In order to make reasonable design for the improvement of comprehensive mechanical properties of RAFM steels, the design system with both machine learning and high-throughput optimization algorithm was established. As the basis of the design system, a dataset of RAFM steels was compiled from previous literatures. Then, feature engineering guided random forests regressors were trained by the dataset and NSGA II algorithm were used for the selection of the optimal solutions from the large-scale solution set with nine composition features and two treatment processing features. The selected optimal solutions by this design system showed prospective mechanical properties, which was also consistent with the physical metallurgy theory. This efficiency design mode could give the enlightenment for the design of other metal structural materials with the requirement of multi-properties.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.102.1-102
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• 2002

In this paper, we propose Self-organizing neurofuzzy networks(SONFN) and discuss their comprehensive design methodology. The proposed SONFN is generated from the mutually combined structure of both neurofuzzy networks (NFN) and polynomial neural networks(PNN) for model identification of complex and nonlinear systems. NFN contributes to the formation of the premise part of the SONFN. The consequence part of the SONFN is designed using PNN. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. We discuss two kinds of SONFN architectures and propose a comprehensive learning algorithm. It is shown that this network...

• Current Optics and Photonics
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• v.8 no.1
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• pp.45-55
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• 2024

A fiber spool with ultra-low vibration sensitivity has been demonstrated for the ultra-narrow-linewidth fiber-stabilized laser by the multi-object orthogonal experimental design method, which can achieve the optimization object and analysis of influence levels without extensive computation. According to a test of 4 levels and 4 factors, an L₁₆ (4⁴) orthogonal table is established to design orthogonal experiments. The vibration sensitivities along the axial and radial directions and the normalized sums of the vibration sensitivities are determined as single objects and comprehensive objects, respectively. We adopt the range analysis of object values to obtain the influence levels of the four design parameters on the single objects and the comprehensive object. The optimal parameter combinations are determined by both methods of comprehensive balance and evaluation. Based on the corresponding fractional frequency stability of ultra-narrow-linewidth fiber-stabilized lasers, we obtain the final optimal parameter combination A3B1C2D1, which can achieve the fiber spool with vibration sensitivities of 10^-12/g magnitude. This work is the first time to use an orthogonal experimental design method to optimize the vibration sensitivities of fiber spools, providing an approach to design the fiber spool with ultra-low vibration sensitivity.

• International Journal of Naval Architecture and Ocean Engineering
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• v.9 no.4
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• pp.373-381
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• 2017

A Collaborative Optimization (CO) methodology for ring-stiffened composite material pressure hull of underwater vehicle is proposed. Structural stability and material strength are both examined. Lamination parameters of laminated plates are introduced to improve the optimization efficiency. Approximation models are established based on the Ellipsoidal Basis Function (EBF) neural network to replace the finite element analysis in layout optimizers. On the basis of a two-level optimization, the simultaneous structure material collaborative optimization for the pressure vessel is implemented. The optimal configuration of metal liner and frames and composite material is obtained with the comprehensive consideration of structure and material performances. The weight of the composite pressure hull decreases by 30.3% after optimization and the validation is carried out. Collaborative optimization based on the lamination parameters can optimize the composite pressure hull effectively, as well as provide a solution for low efficiency and non-convergence of direct optimization with design variables.