WBMOAIS: A novel artificial immune system for multiobjective optimization

This study presents a novel weight-based multiobjective artificial immune system (WBMOAIS) based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. The proposed algorithm follows the elementary structure of opt-aiNET, but has the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function. The fitness assignment has a much lower computational complexity than that based on Pareto ranking, (2) the individuals of the population are chosen from the memory, which is a set of elite solutions, and a local search procedure is utilized to facilitate the exploitation of the search space, and (3) in addition to the clonal suppression algorithm similar to that used in opt-aiNET, a new truncation algorithm with similar individuals (TASI) is presented in order to eliminate similar individuals in memory and obtain a well-distributed spread of non-dominated solutions. The proposed algorithm, WBMOAIS, is compared with the vector immune algorithm (VIS) and the elitist non-dominated sorting genetic system (NSGA-II) that are representative of the state-of-the-art in multiobjective optimization metaheuristics. Simulation results on seven standard problems (ZDT6, SCH2, DEB, KUR, POL, FON, and VNT) show WBMOAIS outperforms VIS and NSGA-II and can become a valid alternative to standard algorithms for solving multiobjective optimization problems.     

ADAPTIVE RANKS CLONE AND k‐NEAREST NEIGHBOR LIST–BASED IMMUNE MULTI‐OBJECTIVE OPTIMIZATION

Artificial immune systems (AIS) are computational systems inspired by the principles and processes of the vertebrate immune system. The AIS‐based algorithms typically exploit the immune system's characteristics of learning and adaptability to solve some complicated problems. Although, several AIS‐based algorithms have proposed to solve multi‐objective optimization problems (MOPs), little focus have been placed on the issues that adaptively use the online discovered solutions. Here, we proposed an adaptive selection scheme and an adaptive ranks clone scheme by the online discovered solutions in different ranks. Accordingly, the dynamic information of the online antibody population is efficiently exploited, which is beneficial to the search process. Furthermore, it has been widely approved that one‐off deletion could not obtain excellent diversity in the final population; therefore, a k‐nearest neighbor list (where k is the number of objectives) is established and maintained to eliminate the solutions in the archive population. The k‐nearest neighbors of each antibody are founded and stored in a list memory. Once an antibody with minimal product of k‐nearest neighbors is deleted, the neighborhood relations of the remaining antibodies in the list memory are updated. Finally, the proposed algorithm is tested on 10 well‐known and frequently used multi‐objective problems and two many‐objective problems with 4, 6, and 8 objectives. Compared with five other state‐of‐the‐art multi‐objective algorithms, namely NSGA‐II, SPEA2, IBEA, HYPE, and NNIA, our method achieves comparable results in terms of convergence, diversity metrics, and computational time.     

基于工学结合的网络综合布线教学改革实践

该文阐述了网络综合布线在课程体系的作用,提出了工学结合的教学设计和教学方法,最后给出教学改革产生的教学效果。     

Predictive models for emission of hydrogen powered car using various artificial intelligent tools

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are developed by means of application of various Artificial Intelligent (AI) models namely; AI software built at the University of Tasmania, back-propagation neural networks with Levenberg–Marquardt algorithm, and adaptive neuro-fuzzy inference systems. These predictions are based on the study of qualitative and quantitative effects of engine process parameters such as mass airflow, engine speed, air-to-fuel ratio, exhaust gas temperature and engine power on the harmful exhaust gas emissions. All AI models show good predictive capability in estimating the emissions. However, excellent accuracy is achieved when using back-propagation neural networks with Levenberg–Marquardt algorithm in estimating emissions for various hydrogen engine operating conditions with the predicted values less than 6% of percentage average root mean square error.     

基于事件驱动的动态免疫分簇路由算法*

为了降低应用于突发事件监测的无线传感器网络的能量消耗,设计实现了一种基于事件驱动的动态免疫分簇路由算法。将生物免疫系统的工作机制应用到无线传感器网络事件驱动的动态分簇算法中,事件作为抗原,传感器节点作为抗体,抗体对抗原有记忆保存的功能,使得相似的抗原再次出现时对事件及时响应。相似事件再次发生且传感器节点符合能量要求时,可以直接调用抗体中的记忆,对事件进行快速建簇,节省了簇的建立过程所消耗的大量能量,增加了网络的数据传输量,延长了网络的生命周期。仿真结果表明,生物免疫机制的学习记忆特性可以有效提高事件驱动的动态分簇算法的网络性能。     

Flash memory efficient LTL model checking

As the capacity and speed of flash memories in form of solid state disks grow, they are becoming a practical alternative for standard magnetic drives. Currently, most solid-state disks are based on NAND technology and much faster than magnetic disks in random reads, while in random writes they are generally not.So far, large-scale LTL model checking algorithms have been designed to employ external memory optimized for magnetic disks. We propose algorithms optimized for flash memory access. In contrast to approaches relying on the delayed detection of duplicate states, in this work, we design and exploit appropriate hash functions to re-invent immediate duplicate detection.For flash memory efficient on-the-fly LTL model checking, which aims at finding any counter-example to the specified LTL property, we study hash functions adapted to the two-level hierarchy of RAM and flash memory. For flash memory efficient off-line LTL model checking, which aims at generating a minimal counterexample and scans the entire state space at least once, we analyze the effect of outsourcing a memory-based perfect hash function from RAM to flash memory.Since the characteristics of flash memories are different to magnetic hard disks, the existing I/O complexity model is no longer sufficient. Therefore, we provide an extended model for the computation of the I/O complexity adapted to flash memories that has a better fit to the observed behavior of our algorithms.     

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．     

基于构件的软件工程及其测试、维护和实践

论述了基于构件的软件工程（ＣＢＳＥ）的基本思想。并对其带来的测试和维护上的新问题及解决方案进行了探讨。最后给出所开发的视频编码软件包的例子。     

Distributed artificial intelligence for FMS scheduling, control and design support

The aim of this paper is to point out some of the abilities of Distributed Artificial Intelligence in the domain of scheduling, control and design support of Flexible Manufacturing Systems. A distributed management system is proposed, based on Distributed Problem Solving, sub-field of Distributed Artificial Intelligence. The basic concepts are the concept of Resource Management Entity to ensure local optimization of the management of resources and the concept of cooperation to provide ability for global and local consistency. The management of resources is associated to activities such as scheduling, control or simulation. It is shown that this system computes not only practicable schedulings, but also presents, on the one hand, some abilities in supporting the design and the robust optimization of Flexible Manufacturing Systems, and, on the other hand, some abilities in supporting real-time control of such systems. This enables, in future works, to design a Distributed Decision Support System for integrated scheduling, control and design support of production systems.     

Re-engineering of the design process for concurrent engineering

According to the requirements of concurrent engineering, three interdependency relationships (uncoupled relationship, coupled relationship and decoupled relationship) between design activities are presented in this paper. And the coupled relationship plays an important role in concurrent product design process. To represent the precedence relationships among design activities, a directed graph is used to describe the design process. And the interdependency relationship between activities is illustrated by a Design Structure Matrix which is the transpose of the accessibility matrix of the corresponding graph. Using the DSM, an algorithm of recognizing the coupled activities during the design process is presented. Moreover, an algorithm to figure out the order levels of activities during the design process is proposed. And both algorithms are illustrated with a die design example.