优进遗传算法及其在化工数据处理中的应用

针对常规遗传算法容易早熟、局部寻优能力差的弱点,提出一种优进遗传算法.该算法以一定的概率引入确定性操作,并采用空间重组的方式改进子代分布,以提高全局寻优的性能.采用的相关技术包括增加单纯形寻优算子、运用改进的交叉算子、自适应地调整交叉率和变异率等.该算法已成功应用于SO2催化氧化反应动力学模型的非线性参数估计.这种优进遗传算法不依赖于问题的具体领域,可应用于各种数据处理和优化领域.

硫酸盐法制浆蒸煮终点预测模型

为了实现制浆蒸煮终点的精确预测,建立了基于广义回归神经网络(GRNN)的预测模型.GRNN具有很强的非线性映射能力,能够根据样本数据逼近自变量与因变量之间隐含的关系,平滑参数的确定是GRNN训练的实质和难点.均衡地兼顾GRNN模型的预测性能与训练可行性,提出了一种平滑参数优化方法.通过分析训练样本分布、恰当地设计适应度函数,运用优进遗传算法(EGA)实现参数寻优.通过实验表明,所建立的制浆蒸煮终点预测模型,预测精度高、稳定性能好.

ANN Model and Learning Algorithm in Fault Diagnosis for FMS

The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm

An Improved Hybrid Genetic Algorithm for Chemical Plant Layout Optimization with Novel Non-overlapping and Toxic Gas Dispersion Constraints

New approaches for facility distribution in chemical plants are proposed including an improved non-overlapping constraint based on projection relationships of facilities and a novel toxic gas dispersion constraint. In consideration of the large number of variables in the plant layout model, our new method can significantly reduce the number of variables with their own projection relationships. Also, as toxic gas dispersion is a usual incident in a chemical plant, a simple approach to describe the gas leakage is proposed, which can clearly represent the constraints of potential emission source and sitting facilities. For solving the plant layout model, an improved genetic algorithm (GA) based on infeasible solution fix technique is proposed, which improves the globe search ability of GA. The case study and experiment show that a better layout plan can be obtained with our method, and the safety factors such as gas dispersion and minimum distances can be well handled in the solution.

A Hybrid Improved Genetic Algorithm and Its Application in Dynamic Optimization Problems of Chemical Processes

The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm （HIGA） for solving dynamic optimization problems is proposed to overcome these defects. Simplex method （SM） is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Ganssian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.

Obsolescence optimization of electronic and mechatronic components by considering dependability and energy consumption

Nowadays, rapid technological progress influences the dependability of equipments and also causes rapid obsolescence. The mechatronic and electronic equipment components are mostly affected by obsolescence. A new challenger unit possesses identical functionalities, but with higher performances. This work aims to find the optimal number of components which should be replaced by new-type units, under budgetary constraints. In this work, the new challenger unit is characterized by lower energy consumption and the optimization steps are based on genetic algorithm (GA). The result shows the importance of this type of replacement in order to economize energy consumption and to deal with obsolescence.

Layout Design-Based Research on Optimization and Assessment Method for Shipbuilding Workshop

The research study proposes to examine a three-dimensional visualization program, emphasizing on improving genetic algorithms through the optimization of a layout design-based standard and discrete shipbuilding workshop. By utilizing a steel processing workshop as an example, the principle of minimum logistic costs will be implemented to obtain an ideological equipment layout, and a mathematical model. The objectiveness is to minimize the total necessary distance traveled between machines. An improved control operator is implemented to improve the iterative efficiency of the genetic algorithm, and yield relevant parameters. The Computer Aided Tri-Dimensional Interface Application （CATIA） software is applied to establish the manufacturing resource base and parametric model of the steel processing workshop. Based on the results of optimized planar logistics, a visual parametric model of the steel processing workshop is constructed, and qualitative and quantitative adjustments then are applied to the model. The method for evaluating the results of the layout is subsequently established through the utilization of AHP. In order to provide a mode of reference to the optimization and layout of the digitalized production workshop, the optimized discrete production workshop will possess a certain level of practical significance.

Multi-parameter optimization design, numerical simulation and performance test of mixed-flow pump impeller

On the basis of the three-dimensional design platform of the mixed-flow pump impellers, an optimization design system was developed in this paper by improving the genetic algorithm with application of both strategies of keeping the optimal individu- al and employing the niche. This system took the highest efficiency of the impeller as the optimization objective and employed P, a0, A0h and A0t, which could directly affect the shape and the position of the blade, as optimization parameters. In addition, loss model was used to obtain fast and accurate prediction of the impeller efficiency. The optimization results illustrated that this system had advantages such as high accuracy and fine convergence, thus to effectively improve the design of the mixed-flow pump impellers. Numerical simulation was applied to determine the internal flow fields of the impeller obtained by optimization design, and to analyze both the relative velocity and the pressure distributions. The test results demonstrated that the mixed flow pump had the highest efficiency of 87.2%, the wide and flat high efficiency operation zone, the relatively wide range of blade angle adjustment, fine cavitation performance and satisfied stability.

Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms

In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.