A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems

This paper presents a novel discrete differential evolution (DDE) algorithm for solving the no-wait flow shop scheduling problems with makespan and maximum tardiness criteria. First, the individuals in the DDE algorithm are represented as discrete job permutations, and new mutation and crossover operators are developed based on this representation. Second, an elaborate one-to-one selection operator is designed by taking into account the domination status of a trial individual with its counterpart target individual as well as an archive set of the non-dominated solutions found so far. Third, a simple but effective local search algorithm is developed to incorporate into the DDE algorithm to stress the balance between global exploration and local exploitation. In addition, to improve the efficiency of the scheduling algorithm, several speed-up methods are devised to evaluate a job permutation and its whole insert neighborhood as well as to decide the domination status of a solution with the archive set. Computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is shown that the proposed DDE algorithm is superior to a recently published hybrid differential evolution (HDE) algorithm [Qian B, Wang L, Huang DX, Wang WL, Wang X. An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers. Computers & Operations Research 2009;36(1):209–33] and the well-known multi-objective genetic local search algorithm (IMMOGLS2) [Ishibuchi H, Yoshida I, Murata T. Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation 2003;7(2):204–23] in terms of searching quality, diversity level, robustness and efficiency. Moreover, the effectiveness of incorporating the local search into the DDE algorithm is also investigated.     

基于和声搜索和模拟退火的混合算法解决TSP问题

和声搜索是一种新的启发式优化算法,算法模拟了音乐创作中乐师们凭借自己的记忆,通过反复地调整乐队中各乐器的音调,最终能达到一个美妙的和声状态的过程.针对和声搜索算法容易陷入局部最优的缺陷,将其与模拟退火算法结合,得到了两种混合算法.实验表明混合算法针对TSP问题具有较高的求解质量.     

Evaluation of the combination mode of azoles antifungal inhibitors with CACYP51 and the influence of Site-directed mutation

14α-demethylase (CYP51) is an essential metabolic enzyme for fungal survival and has been considered as an interesting target for the development of new antifungal inhibitors. Azoles antifungal inhibitors in the treatment of fungal diseases are good candidates via the interaction with the target enzyme CYP51 of fungus. In the study, we constructed the homology model for Candida albicans CYP51 (CACYP51) and analyzed the active site. In order to better understand the structural characteristics of azoles inhibitors and combination mode, the common feature pharmacophore model and the molecular docking were performed. The results suggest that the azoles inhibitors consist of three chemical features: the aromatic groups, phenyl groups and the azoles groups. The aromatic groups of inhibitors occupy the upper of active pocket, the phenyl groups and azoles groups occupy the bottom of active pocket. Further validation studies found these amino acid residues Tyr118, His310 and Ser378 play an important role in the substrate binding, and these amino acid residues with site-directed mutation will weaken the combining ability of the inhibitors.     

Multiple Instance Learning via Semi-supervised Laplacian TSVM

Multiple instance learning attempts to learn from a training set consists of labeled bags each containing many unlabeled instances. In previous works, most existing algorithms mainly pay attention to the ‘most positive’ instance in each positive bag, but ignore the other instances. For utilizing these unlabeled instances in positive bags, we present a new multiple instance learning algorithm via semi-supervised laplacian twin support vector machines (called Miss-LTSVM). In Miss-LTSVM, all instances in positive bags are used in the manifold regularization terms for improving the performance of classifier. For verifying the effectiveness of the presented method, a series of comparative experiments are performed on seven multiple instance data sets. Experimental results show that the proposed method has better classification accuracy than other methods in most cases.     

Robust Stochastic Stability and Control for Uncertain Singular Markovian Jump Systems with Multiplicative Noise

This paper focuses on the problems of robust stability and stabilization and robust control for uncertain singular Markovian jump systems with (x,v)‐dependent noise. The parameter uncertainties appearing in state, input, disturbance as well as diffusion terms are assumed to be time‐varying but norm‐bounded. Based on the approach of generalized quadratic stability, the memoryless state feedback controller is designed for the robust stabilization problem, which ensures that the resulting closed‐loop system has an impulse‐free solution and is asymptotically stable in the mean square. Furthermore, the results of robust control problem are derived. The desired state feedback controller is presented, which not only meets the requirement of robust stabilization but also satisfies a prescribed performance level. The obtained results are formulated in terms of strict LMIs. What we have obtained can be viewed as corresponding extensions of existing results on uncertain singular systems. A numerical example is finally given to demonstrate the application of the proposed method.     

一种改进的各向异性扩散去噪模型

为了既能有效地去除噪声,又能够较好地保持图像的边缘以及重要的细节信息,在Perona和Malik提出的各向异性扩散模型(P-M模型)的基础上,通过对扩散方程中扩散函数的改进,提出了一种具备自适应性的去噪扩散模型,该模型对图像去噪处理更加高效。改进的扩散函数在梯度较小时为一个常数,大于某个阈值后变为单调递减函数,直至某个梯度时递减为零。以上扩散函数特性使各向异性扩散模型能够达到在同质区加速平滑、在边缘区停止平滑的目的。实验结果表明,改进的扩散模型是一种更为理想的保边缘平滑模型。     

A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem

In this paper, a discrete artificial bee colony (DABC) algorithm is proposed to solve the lot-streaming flow shop scheduling problem with the criterion of total weighted earliness and tardiness penalties under both the idling and no-idling cases. Unlike the original ABC algorithm, the proposed DABC algorithm represents a food source as a discrete job permutation and applies discrete operators to generate new neighboring food sources for the employed bees, onlookers and scouts. An efficient initialization scheme, which is based on the earliest due date (EDD), the smallest slack time on the last machine (LSL) and the smallest overall slack time (OSL) rules, is presented to construct the initial population with certain quality and diversity. In addition, a self adaptive strategy for generating neighboring food sources based on insert and swap operators is developed to enable the DABC algorithm to work on discrete/combinatorial spaces. Furthermore, a simple but effective local search approach is embedded in the proposed DABC algorithm to enhance the local intensification capability. Through the analysis of experimental results, the highly effective performance of the proposed DABC algorithm is shown against the best performing algorithms from the literature.     

A novel supervised dimensionality reduction algorithm: Graph-based Fisher analysis

In this paper, a novel supervised dimensionality reduction (DR) algorithm called graph- based Fisher analysis (GbFA) is proposed. More specifically, we redefine the intrinsic and penalty graph and trade off the importance degrees of the same-class points to the intrinsic graph and the importance degrees of the not-same-class points to the penalty graph by a strictly monotone decreasing function; then the novel feature extraction criterion based on the intrinsic and penalty graph is applied. For the non-linearly separable problems, we study the kernel extensions of GbFA with respect to positive definite kernels and indefinite kernels, respectively. In addition, experiments are provided for analyzing and illustrating our results.     

An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan

This paper deals with a variant of flowshop scheduling, namely, the hybrid or flexible flowshop with sequence dependent setup times. This type of flowshop is frequently used in the batch production industry and helps reduce the gap between research and operational use. This scheduling problem is NP-hard and solutions for large problems are based on non-exact methods. An improved genetic algorithm (GA) based on software agent design to minimise the makespan is presented. The paper proposes using an inherent characteristic of software agents to create a new perspective in GA design. To verify the developed metaheuristic, computational experiments are conducted on a well-known benchmark problem dataset. The experimental results show that the proposed metaheuristic outperforms some of the well-known methods and the state-of-art algorithms on the same benchmark problem dataset.     

广告英语中的修辞语言美

广告英语是激烈的竞争以及设计者的匠心独具、精雕细琢的产物。它不仅在词语的运用、句型的谋划上巧妙精细,在修辞的运用上(尤其是对比喻、拟人、双关、夸张、仿拟的灵活运用)更为讲究,而产生新颖别致、形象生动、引人注目的效果。