多目标车辆路径的遗传算法

传统的单目标遗传算法运行一次只能得到一个解,而多目标遗传算法运行一次可以得到一个解集,多个解可以为决策者提供更多的选择余地,作出更好的决策。本算法通过设计新的改进遗传算子,进一步提高了算法的性能,并设计采用擂台法则构建非支配集,降低了时间复杂度。通过实验验证表明,此算法能有效的解决车辆路径问题。     

Computing all efficient solutions of the biobjective minimum spanning tree problem

A common way of computing all efficient (Pareto optimal) solutions for a biobjective combinatorial optimisation problem is to compute first the extreme efficient solutions and then the remaining, non-extreme solutions. The second phase, the computation of non-extreme solutions, can be based on a “k-best” algorithm for the single-objective version of the problem or on the branch-and-bound method. A k-best algorithm computes the k-best solutions in order of their objective values. We compare the performance of these two approaches applied to the biobjective minimum spanning tree problem. Our extensive computational experiments indicate the overwhelming superiority of the k-best approach. We propose heuristic enhancements to this approach which further improve its performance.     

Evaluation of neural networks-based controllers in batch polymerisation of methyl methacrylate

The importance of batch reactors in today's process industries cannot be overstated. Thus said, it is important to optimise their operation in order to consistently achieve products of high quality while minimising the production of undesirables. In processes like polymerisation, these reactors are responsible for a greater number of products than other reactor types and the need for optimal operation is therefore greater.

An approach based on an offline dynamic optimisation and online control strategy is used in this work to generate optimal set point profiles for the batch polymerisation of methyl methacrylate. Dynamic optimisation is carried out from which controller set points to attain desired polymer molecular end point characteristics are achieved. Temperature is the main variable to be controlled, and this is done over finite discrete intervals of time.

For on-line control, we evaluate the performance of neural networks in two controllers used to track the derived optimal set points for the system. The controllers are generic model control (GMC), ([P.L. Lee, G.R. Sullivan, Generic model control, Comput. Chem. Eng. 12(6) (1998) 573–580]) and the neural network-based inverse model-based control (IMBC), ([M.A. Hussain, L.S. Kershenbaum, Implementation of an inverse model based control strategy using neural networks on a partially simulated exothermic reactor, Trans. IchemE 78(A) (2000) 299–311]). Although the GMC is a model-based controller, neural networks are used to estimate the heat release within its framework for on-line control. Despite the application of these two controllers to general batch reactors, no published work exists on their application to batch polymerisation in the literature. In this work, the performance of the neural networks within each controller's algorithm for tracking and setpoint regulation of the optimal trajectory and in robustness tests on the system is evaluated.     

An integrated multi-objective immune algorithm for optimizing the wire bonding process of integrated circuits

Optimization of the wire bonding process of an integrated circuit (IC) is a multi-objective optimization problem (MOOP). In this research, an integrated multi-objective immune algorithm (MOIA) that combines an artificial immune algorithm (IA) with an artificial neural network (ANN) and a generalized Pareto-based scale-independent fitness function (GPSIFF) is developed to find the optimal process parameters for the first bond of an IC wire bonding. The back-propagation ANN is used to establish the nonlinear multivariate relationships between the wire boning parameters and the multi-responses, and is applied to generate the multiple response values for each antibody generated by the IA. The GPSIFF is then used to evaluate the affinity for each antibody and to find the non-dominated solutions. The “Error Ratio” is then applied to measure the convergence of the integrated approach. The “Spread Metric” is used to measure the diversity of the proposed approach. Implementation results show that the integrated MOIA approach does generate the Pareto-optimal solutions for the decision maker, and the Pareto-optimal solutions have good convergence and diversity performance.     

一种改进的小生境遗传算法在多目标优化中的应用

引入个体迁徙和捕猎行为的模拟,改进小生境遗传算法的思想,以加快Pareto最优解的收敛速度和保证解的多样性,使得改进的算法更适合于多目标优化求解.计算实例表明,与SPEA算法相比,所提出的算法更优越.     

一种基于多亲遗传机制的多目标优化算法

给出了进化个体之间的关系和非支配集中不同个体之间的相关性质,参考快速排序的思想,提出了一种有效的构造非支配集的算法.在此基础上,将多亲遗传算法与改进的快速排序构造非支配集的算法相结合,提出了一种基于多亲遗传机制的多目标优化算法.最后对提出算法进行了分析,采用了测试函数进行了仿真实验,获得了理想的实验结果.     

基于协同交互式遗传算法的复杂产品概念设计

针对复杂产品概念设计中的方案求解问题,建立了基于进化思想的求解过程模型,提出了一种新的基于协同进化算法与交互式遗传算法相结合的复杂产品概念设计方法.针对手机概念设计中功能设计的特点提出了变长度编码和两层编码的混合编码方式.实例表明,该方法对多目标、人机交互的复杂产品概念设计方案求解是有效的.     

基于同构化信息融合的异构不确定多属性决策新模型与新算法

不确定多属性决策过程中,现有两大困难:(1)如何较好地表达和处理具有不确定性的属性评价信息;(2)如何将基于多样性评判准则的多准则评价结果进行信息融合,并获得更合理的综合评价结论.基于同构化思想,针对学术界最近才提出的一种能较好地处理具有多信息来源模糊信息的新数学模型"多值直觉模糊集模型",研究了多值直觉模糊集的隶属度与非隶属度的综合评判新课题与新方法;提出了兼有不确定语言型与区间型的异构风险型多属性决策新问题与新模型,构造了基于同构化信息融合的异构不确定多属性决策新模型与新算法.     

Hierarchical classification of protein function with ensembles of rules and particle swarm optimisation

This paper focuses on hierarchical classification problems where the classes to be predicted are organized in the form of a tree. The standard top-down divide and conquer approach for hierarchical classification consists of building a hierarchy of classifiers where a classifier is built for each internal (non-leaf) node in the class tree. Each classifier discriminates only between its child classes. After the tree of classifiers is built, the system uses them to classify test examples one class level at a time, so that when the example is assigned a class at a given level, only the child classes need to be considered at the next level. This approach has the drawback that, if a test example is misclassified at a certain class level, it will be misclassified at deeper levels too. In this paper we propose hierarchical classification methods to mitigate this drawback. More precisely, we propose a method called hierarchical ensemble of hierarchical rule sets (HEHRS), where different ensembles are built at different levels in the class tree and each ensemble consists of different rule sets built from training examples at different levels of the class tree. We also use a particle swarm optimisation (PSO) algorithm to optimise the rule weights used by HEHRS to combine the predictions of different rules into a class to be assigned to a given test example. In addition, we propose a variant of a method to mitigate the aforementioned drawback of top-down classification. These three types of methods are compared against the standard top-down hierarchical classification method in six challenging bioinformatics datasets, involving the prediction of protein function. Overall HEHRS with the rule weights optimised by the PSO algorithm obtains the best predictive accuracy out of the four types of hierarchical classification method.     

A multi-objective strategy in genetic algorithms for gene selection of gene expression data

A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach that used a single-objective strategy in a genetic algorithm. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008