Accuracy Of Optimization Results Research Articles

Development of a Composite Model for Simulating Landscape Pattern Optimization Allocation: A Case Study in the Longquanyi District of Chengdu City, Sichuan Province, China

The simulation of landscape pattern optimization allocation (LPOA) to achieve ecological security is an important issue when constructing regional ecological security patterns. In this study, an LPOA model was developed by integrating a binary logistic regression model and a nonlinear programming model with a particle swarm optimization algorithm in order to consider the complexity of landscape pattern optimization in terms of the quantitative structure and spatial layout optimization, integrating the landscape suitability and factors that influence landscape patterns, and under constraints to maximize the economic, ecological, and comprehensive benefits of landscape patterns. The model was employed to simulate the LPOA in the Longquanyi District of Chengdu City, Sichuan Province, China. The model successfully obtained an appropriate combination of the landscape quantitative structure and spatial layout, as well as effectively integrating the landscape suitability and factors that influence the landscape pattern. Thus, the model addressed the problems of previous studies, such as neglecting the coupling between quantitative structure optimization and spatial layout optimization, ignoring the macrofactors that affect landscape patterns during optimization modeling, and initializing particles without considering the suitability of the landscape. Furthermore, we assessed and analyzed the accuracy and feasibility of the landscape pattern spatial layout optimization results, where the results showed that the overall accuracy of the optimization results was 84.98% with a Kappa coefficient of 0.7587, thereby indicating the good performance of the model. Moreover, the simulated optimization allocation scheme for the landscape pattern was consistent with the actual situation. Therefore, this model can provide support and a scientific basis for regional landscape pattern planning, land use planning, urban planning, and other related spatial planning processes.

Multifidelity Multidisciplinary Design Optimization of Integral Solid Propellant Ramjet Supersonic Cruise Vehicles

Integral solid propellant ramjet (ISPR) supersonic cruise vehicles share the characteristic that they are highly integrated configurations. The traditional design of vehicles cannot achieve a balance between computational expense and accuracy. A multifidelity multidisciplinary design optimization (MDO) platform has been developed in this study. The focus of the platform is on ISPR supersonic cruise vehicles. Firstly, codes of discipline with different levels of fidelity (LoF) were established, such as geometry, aerodynamics, radar cross-section calculations, propulsion, mass, and trajectory discipline codes. Secondly, two MDO frameworks were constructed through discipline codes. A low LoF MDO framework is suitable for conceptual design, and a medium LoF MDO framework is suitable for preliminary design. Finally, taking the optimization problem with the minimum overall detection probability of flight trajectory as an example, the low LoF framework first explores the entire design space to achieve the mission requirements, and then, the medium LoF MDO framework accepts the low LoF framework optimization parameters. Hence, the optimization target is reached with more detailed parameters and higher fidelity. Additionally, an example for a solid propellant missile with minimum total mass is tested by the platform. The study results show that the multifidelity MDO framework not only exploits interactions between the disciplines but also improves the accuracy of optimization results and reduces the iteration time.

On the effect of numerical noise in approximate optimization of forming processes using numerical simulations

The coupling of Finite Element (FE) simulations with approximate optimization techniques is becoming increasingly popular in forming industry. By doing so, it is implicitly assumed that the optimization objective and possible constraints are smooth functions of the design variables and, in case of robust optimization, design and noise variables. However, non-linear FE simulations are known to introduce numerical noise caused by the discrete nature of the simulation algorithms, e.g. errors caused by re-meshing, time-step adjustments or contact algorithms. The subsequent usage of metamodels based on such noisy data reduces the prediction quality of the optimization routine and is known to even magnify the numerical errors. This work provides an approach to handle noisy numerical data in approximate optimization of forming processes, covering several fundamental research questions in dealing with numerical noise. First, the deteriorating effect of numerical noise on the prediction quality of several well-known metamodeling techniques is demonstrated using an analytical test function. Next, numerical noise is quantified and its effect is minimized by the application of local approximation and regularization techniques. A general approximate optimization strategy is subsequently presented and coupling with a sequential update algorithm is proposed. The strategy is demonstrated by the sequential deterministic and robust optimization of 2 industrial metal forming processes i.e. a V-bending application and a cup-stretching application. Although numerical noise is often neglected in practice, both applications in this work show that the general awareness of its presence is highly important to increase the overall accuracy of optimization results.

An Optimization Method for Optimal Engineering Structure Design Combining Intelligent Optimization Algorithm and CAE Code

This paper presents an optimization method for optimal engineering structure design. An interface procedure is essentially developed to combine the intelligent optimization algorithm and computer aided engineering (CAE) code. An optimization example is carried out to minimize the interlaminar normal stress of a laminate which affect the delamination failure of a laminate via arranging the stacking sequence. The analytical solution is calculated to validate the accuracy of optimization results.

An online self-adapting control approach for bell annealers

A suit of online self-adapting control (OSAC) approach has been developed to predict and optimize annealing craft system. The approach consists of three critical parts including prediction module, self-adapting optimization module, and self-learning amendment module. Firstly, the prediction module and self-adapting optimization module are based on the modeling methods. The self-adapting optimization module consists of two parts including “reappearance of annealed process” and “optimization of subsequent annealing process”. Secondly, the self-learning amendment module, based on furnace atmosphere, equipment performance, and compensation coefficients, is designed to improve the accuracy of optimization results. The results obtained from the proposed approach, usually finished in about 3 min, are in good agreement with the test values, such as the deviation of temperature for hot-spot and cold-spot are within 10 K, the relative errors are within 1.1%, and the accuracy of annealing for heating period is increased by using self-learning amendment module.