基于改进NSGA-Ⅱ算法的乙烯裂解炉操作优化

现有的乙烯裂解炉优化通常只针对两个目标函数即产物乙烯和丙烯的收率，并且采用遗传算法的收敛效果一般，故提出一种基于改进NSGA-Ⅱ算法来研究一个多目标运行的解决方案，以此来解决乙烯裂解炉的固定周期操作优化问题，即在增大产物乙烯和丙烯收率的同时减少原料以及蒸汽流量来提高整体运行状况。把具体问题量化为数学模型，分析了原料气烃比、原料流量、出口温度对乙烯和丙烯收率的影响。实验结果表明，相较于原有操作条件，提出的优化方案具有良好的可行性。     

Multi-objective optimization for efficient modeling and improvement of the high temperature PEM fuel cell based Micro-CHP system

Fuel cells due to different useful features such as high efficiency, low pollution, noiselessness, lack of moving parts, variety of fuels used and wide range of capacity of these sources can be the main reasons for their tendency to use them in different applications. In this study, the application of a high temperature proton exchange membrane fuel cell (HT-PEMFC) in a combined heat and power (CHP) plant has been analyzed. This study presents a multi-objective optimization method to provide an optimal design parameters for the HT-PEMFC based micro-CHP during a 14,000 h lifetime by considering the effect of degradation. The purpose is to optimize the net electrical efficiency and the electrical power generation. For the optimization process, different design parameters including auxiliary to process fuel ratio, anodic stoichiometric ratio, steam to carbon ratio, and fuel partialization level have been employed. For optimization, A new technique based on Tent mapping and Lévy flight mechanism, called improved collective animal behavior (ICAB) algorithm has been employed to solve the algorithm premature convergence shortcoming. Experimental results of the proposed method has been applied to the data of a practical plant (Sidera30) for analyzing the efficiency of the proposed ICAB based system, it is compared with normal condition and another genetic algorithm based method for this purpose. Final results showed that the difference between the maximum electrical power production under normal condition and ICAB based condition changes from 2.5 kW when it starts and reaches to its maximum value, 3.0 kW, after 14,000 h lifetime. It is also concluded that the cumulative average for the normal and the ICAB based algorithm are 24.01 kW and 27.04 kW, respectively which showed about 3.03 kW cumulative differences.     

基于GA-MVFOSM法的DSA250型受电弓运动可靠性分析

为分析运动副磨损与杆件尺寸不确定性2种因素对受电弓运动可靠性的影响，提出一种基于遗传一次二阶矩法(GA-MVFOSM)的受电弓运动可靠性计算方法。首先，构建受电弓运动学方程，应用Archard磨损理论建立其运动副磨损可靠性模型。在此基础上，考虑运动副磨损与杆件尺寸的不确定性，结合可靠性理论建立受电弓运动可靠性模型。其次，利用遗传算法优化一次二阶矩法，提出GA-MVFOSM算法，用以求解受电弓的运动可靠度。最后，以DSA250型受电弓为研究对象，利用所提方法分析其运动可靠性，并与传统方法进行对比。分析结果表明:各运动副磨损量分别在0.230 0 mm、0.136 6 mm、0.006 6 mm内时，运动副磨损可靠度处于较高水平，2种因素耦合情况下弓头运动可靠度最小值为0.964 7；基于遗传一次二阶矩的计算方法可以有效提高传统方法计算精度，为提高受电弓工作可靠性提供参考依据。     

高速冲压生产线上下料冗余机械臂的运动学分析

在应用于高速冲压生产线冗余机械臂姿态规划和轨迹优化过程中,以兼顾上下料效率与轨迹的平稳性为目的,对冗余机器臂的加加速度进行约束,基于旋量理论建立运动学模型,得到正运动学方程,采用遗传算法对冗余机械臂进行逆运动学分析,结合五次B样条插值方法,对末端执行器运动轨迹的加加速度进行优化。结果表明,该方案可有效提高上下料效率,保证冗余机械臂上下料过程运行平稳。     

GA-based learning for rule identification in fuzzy neural networks

Employing an effective learning process is a critical topic in designing a fuzzy neural network, especially when expert knowledge is not available. This paper presents a genetic algorithm (GA) based learning approach for a specific type of fuzzy neural network. The proposed learning approach consists of three stages. In the first stage the membership functions of both input and output variables are initialized by determining their centers and widths using a self-organizing algorithm. The second stage employs the proposed GA based learning algorithm to identify the fuzzy rules while the final stage tunes the derived structure and parameters using a back-propagation learning algorithm. The capabilities of the proposed GA-based learning approach are evaluated using a well-examined benchmark example and its effectiveness is analyzed by means of a comparative study with other approaches. The usefulness of the proposed GA-based learning approach is also illustrated in a practical case study where it is used to predict the performance of road traffic control actions. Results from the benchmarking exercise and case study effectively demonstrate the ability of the proposed three stages learning approach to identify relevant fuzzy rules from a training data set with a higher prediction accuracy than alternative approaches.     

Profit maximization of TSP through a hybrid algorithm

Here a new model of Traveling Salesman Problem (TSP) with uncertain parameters is formulated and solved using a hybrid algorithm. For this TSP, there are some fixed number of cities and the costs and time durations for traveling from one city to another are known. Here a Traveling Salesman (TS) visits and spends some time in each city for selling the company’s product. The return and expenditure at each city are dependent on the time spent by the TS at that city and these are given in functional forms of t. The total time limit for the entire tour is fixed and known. Now, the problem for the TS is to identify a tour program and also to determine the stay time at each city so that total profit out of the system is maximum. Here the model is solved by a hybrid method combining the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The problem is divided into two subproblems where ACO and PSO are used successively iteratively in a generation using one’s result for the other. Numerical experiments are performed to illustrate the models. Some behavioral studies of the models and convergences of the proposed hybrid algorithm with respect to iteration numbers and cost matrix sizes are presented.     

A multi-start central force optimization for global optimization

Central force optimization (CFO) is an efficient and powerful population-based intelligence algorithm for optimization problems. CFO is deterministic in nature, unlike the most widely used metaheuristics. CFO, however, is not completely free from the problems of premature convergence. One way to overcome local optimality is to utilize the multi-start strategy. By combining the respective advantages of CFO and the multi-start strategy, a multi-start central force optimization (MCFO) algorithm is proposed in this paper. The performance of the MCFO approach is evaluated on a comprehensive set of benchmark functions. The experimental results demonstrate that MCFO not only saves the computational cost, but also performs better than some state-of-the-art CFO algorithms. MCFO is also compared with representative evolutionary algorithms. The results show that MCFO is highly competitive, achieving promising performance.     

Application of Vis–NIR hyperspectral imaging in classification between fresh and frozen-thawed pork Longissimus Dorsi muscles

Fresh and frozen-thawed (F-T) pork meats were classified by Vis–NIR hyperspectral imaging. Eight optimal wavelengths (624, 673, 460, 588, 583, 448, 552 and 609 nm) were selected by successive projections algorithm (SPA). The first three principal components (PCs) obtained by principal component analysis (PCA) accounted for over 99.98% of variance. Gray-level-gradient co-occurrence matrix (GLGCM) was applied to extract 45 textural features from the PC images. The correct classification rate (CCR) was employed to evaluate the performance of the partial least squares-discriminate analysis (PLS-DA) models, by using (A) the reflected spectra at full wavelengths and (B) those at the optimal wavelengths, (C) the extracted textures based on the PC images, and (D) the fused variables combining spectra at the optimal wavelengths and textures. The results showed that the best CCR of 97.73% was achieved by applying (D), confirming the high potential of textures for fresh and F-T meat discrimination.     

Maximizing lifetime in wireless sensor networks with multiple sensor families

Wireless sensor networks are generally composed of a large number of hardware devices of the same type, deployed over a region of interest in order to perform a monitoring activity on a set of target points. Nowadays, several different types of sensor devices exist, which are able to monitor different aspects of the region of interest (including sound, vibrations, proximity, chemical contaminants, among others) and may be deployed together in a heterogeneous network. In this work, we face the problem of maximizing the amount of time during which such a network can remain operational, while maintaining at all times a minimum coverage guarantee for all the different sensor types. Some global regularity conditions in order to guarantee a fair level of coverage for each sensor type to each target are also taken into account in a second variant of the proposed problem. For both problem variants we developed an exact approach, which is based on a column generation algorithm whose subproblem is either solved heuristically by means of a genetic algorithm or optimally by an appropriate ILP formulation. In our computational tests the proposed genetic algorithm is shown to be able to dramatically speed up the procedure, enabling the resolution of large-scale instances within reasonable computational times.     

Electromagnetism-like algorithms for optimized tool path planning in 5-axis flank machining

Optimization of tool path planning using metaheuristic algorithms such as ant colony systems (ACS) and particle swarm optimization (PSO) provides a feasible approach to reduce geometrical machining errors in 5-axis flank machining of ruled surfaces. The optimal solutions of these algorithms exhibit an unsatisfactory quality in a high-dimensional search space. In this study, various algorithms derived from the electromagnetism-like mechanism (EM) were applied. The test results of representative surfaces showed that all EM-based methods yield more effective optimal solutions than does PSO, despite a longer search time. A new EM-MSS (electromagnetism-like mechanism with move solution screening) algorithm produces the most favorable results by ensuring the continuous improvement of new searches. Incorporating an SPSA (simultaneous perturbation stochastic approximation) technique further improves the search results with effective initial solutions. This work enhances the practical values of tool path planning by providing a satisfactory machining quality.