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.     

Using frameworks to develop a distributed conferencing system: an experience report

Application frameworks are a powerful means to reduce software development costs while improving quality. However, at the same time they are difficult to select and understand, as well as hard to learn, use, and debug effectively and efficiently. In this paper we report the story of eConference, a distributed conferencing system that was developed as part of a broader research effort. Here we discuss the lessons learned from the evolution of our conferencing tool over four generations, which have been necessary to find good frameworks and build a flexible distributed tool. Copyright © 2009 John Wiley & Sons, Ltd.     

面向本体的服务组合差分进化策略

针对目前Web服务描述服务功能的语义不够深入而使服务识别组合能力欠佳的问题,提出一种面向本体的语义服务功能描述方法,建立本体间的映射关系模型和服务质量关系模型,通过改进差分进化算法求解,得到一种较好的服务组合识别方法。在九寨沟旅游管理信息系统中的应用表明,该方法比传统的方法优越。     

邻域结构为复杂网络的粒子群算法*

为避免早熟收敛和局部最优,设计了一种基于复杂网络进行个体交互的粒子群算法(CNS-PSO)。该算法在粒子与网络节点间建立映射关系,并根据节点的邻居集合,获得粒子的动态飞行邻居。每个飞行邻居集合是一个独立又彼此联系的进化小社会。在CNS-PSO中,每个粒子的位置更新策略不仅考虑了认知部分及社会的信息共享,还考虑了小社会内和小社会间的信息交流。在8个测试函数上,将CNS-PSO与标准PSO进行了对比,在不同维度的大多数函数上,前者的性能均优后者。说明具有无标度网络邻域结构的CNS-PSO算法具有较强的避免早熟和逃逸局部最优的能力。     

An interactive approach based on a discrete differential evolution algorithm for a class of integer bilevel programming problems

This paper presents an interactive approach based on a discrete differential evolution algorithm to solve a class of integer bilevel programming problems, in which integer decision variables are controlled by an upper-level decision maker and real-value or continuous decision variables are controlled by a lower-level decision maker. Using the Karush--Kuhn–Tucker optimality conditions in the lower-level programming, the original discrete bilevel formulation can be converted into a discrete single-level nonlinear programming problem with the complementarity constraints, and then the smoothing technique is applied to deal with the complementarity constraints. Finally, a discrete single-level nonlinear programming problem is obtained, and solved by an interactive approach. In each iteration, for each given upper-level discrete variable, a system of nonlinear equations including the lower-level variables and Lagrange multipliers is solved first, and then a discrete nonlinear programming problem only with inequality constraints is handled by using a discrete differential evolution algorithm. Simulation results show the effectiveness of the proposed approach.     

基于差分蜂群算法的电力系统经济负荷分配

针对电力系统经济负荷分配问题,提出一种有效的差分蜂群算法．受差分进化算法的启发,该算法基于差分进化操作改进了雇佣蜂的搜索方式,提高了探索能力和收敛速度．此外,提出一种有效的修复机制以保证新个体的可行性．该算法在带有阀点效应和多燃料特征的典型电力系统经济负荷分配问题上进行了测试．仿真结果验证了所提算法的有效性．     

基于差分进化支持向量机的移动机器人可通过度预测

提出了一种移动机器人可通过度预测方法.给出了基于相对震动强度的可通度描述.通过提取典型地表图像的色彩和纹理特征并测量机器人通过该地表的相对震动强度建立训练样本集.使用差分进化算法优化支持向量机模型参数形成差分进化支持向量机对训练样本和相对震动强度进行拟合.在移动机器人运行过程中,线性分割前方地表图像形成预测子区域,通过...     

基于差异进化的克隆选择算法

针对免疫算法在全局优化过程中多样性不足的问题,将差异进化引入克隆变异操作中,提出了一个新的改进的克隆选择算法——基于差异进化的克隆选择算法(DECSA),算法将差异进化和克隆超变异相结合,促进了抗体与抗体之间的信息融合,使得子代抗体继承父代抗体的信息的同时,携带着不同父代个体信息,丰富了抗体种群的多样性,实现了在同一父代抗体周围的多个方向同时进行全局和局部搜索。对13个标准测试函数的测试结果及与已有的算法的比较表明,该算法表现出较好的局部搜索和全局搜索能力。     

Evolving Teams of Predictors with Linear Genetic Programming

This paper applies the evolution of GP teams to different classification and regression problems and compares different methods for combining the outputs of the team programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a common decision and (2) a realnumbered vector (the representation of evolution strategies) of weights is evolved with each term in parallel. The cooperative team approach results in an improved training and generalization performance compared to the standard GP method. The higher computational overhead of team evolution is counteracted by using a fast variant of linear GP.