基于人工免疫算法的精铜板带加工配料优化

为循环利用铜资源、降低成本、减少烧损,且满足不同牌号旧料可代用性等实际配料要求,建立了多目标实时配料模型,并进行模型转换,设计了精铜板带加工配料优化的人工免疫算法.重点研究了抗体表示、抗体与抗原及抗体与抗体亲和力的计算、初始种群产生等关键环节,给出了免疫算法的具体实现步骤.实验结果表明,与传统遗传算法相比,人工免疫算法可获得具有代表性的多个满意解,具有较强的多样性,便于在实际投料操作中选择.     

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.     

An immune inspired co-evolutionary affinity network for prefetching of distributed object

In distributed object computing (DOC) containers, cache strategy as a passive approach improves the performance by caching the recently accessed objects. By the advent of large-scale enterprise applications of DOC, caching methods fail to keep pace with the increasing importance of performance and the increasing scale of DOC system. Prefetching is an effective approach to improve the performance. It generates and stores pages or objects in caches in advance, by predicting the requests. In the current DOC container, prefetching strategy is not supported and seldom studied in the literature. The immune system with faster secondary response and its affinity network inspire applying the immune mechanisms to build holistic model of DOC for performance improvement. In this study, a co-evolutionary affinity network (CEA-Net) is proposed for prefetching distributed objects. In CEA-Net, objects are antibodies and computation tasks are antigens. Invocation relations among object classes are modeled by the immune network of antibody genotypes. Multiple affinity measures among antibody, antigen, genotypes of antibody and antigen, genotype set and antibody population are defined to model the complex relations among distributed objects. Especially in the antibody population, immune principles including clonal proliferation, immune memory, immune toleration and elimination are designed to add evolutionary features to the antibody population. Based on CEA-Net, the prefetching architecture for DOC is built including 5 main procedures, Network Abstractor, Access Recorder, Object Factory, Cache Engine and Prefetch Engine. Finally, the experimental study shows the promising access performance and the evolutionary features of CEA-Net. CEA-Net is instructive for the future design of high performance DOC containers, such as WebSphere Application Server and BEA WebLogic Application Server.     

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.     

基于优化目标可调控的免疫网络算法的分析与应用

针对目前几种免疫网络模型在数据聚类方面应用的一些不足,在aiNet免疫算法的基础上结合函数优化的思想提出一种基于目标可调控的免疫模型。并在算法中给出目标控制函数,和细胞记忆库的概念。本算法提高了免疫学习质量并从整体上对免疫网络进行优化。     

基于校园网安全的主动防御系统部署

随着校园网办公自动化的不断普及,校园网络的安全问题也越来越受到更多地重视,它关系到学校的教学、科研和管理地工作是否能够正常进行。而传统的校园网安全防御体系已经很难适应当前复杂、大规模的攻击行为。虽然主动防御系统还存在着一些技术上的不足,但是它能够主动地检测、分析这些攻击行为,并对其及时阻断并提高系统的免疫力。该文以P2DR为实施模型,并结合现有的安全防御技术,构建了针对校园网存在的安全问题的主动防御系统及其相关的实施策略。     

网络安全防御技术浅析

随着互联网应用的不断深入,计算机系统安全和网络安全受到的威胁日益增加,本文介绍了网络防御技术研究的必要性,并对网络安全相关防御技术进行了分析,最后对网络防御技术的发展趋势作出了展望。     

A hybrid immune simulated annealing algorithm for the job shop scheduling problem

A hybrid simulated annealing algorithm based on a novel immune mechanism is proposed for the job shop scheduling problem with the objective of minimizing total weighted tardiness. The proposed immune procedure is built on the following fundamental idea: the bottleneck jobs existing in each scheduling instance generally constitute the key factors in the attempt to improve the quality of final schedules, and thus, the sequencing of these jobs needs more intensive optimization. To quantitatively describe the bottleneck job distribution, we design a fuzzy inference system for evaluating the bottleneck level (i.e. the criticality) of each job. By combining the immune procedure with a simulated annealing algorithm, we design a hybrid optimization algorithm which is subsequently tested on a number of job shop instances. Computational results for different-sized instances show that the proposed hybrid algorithm performs effectively and converges fast to satisfactory solutions.     

Information fusion in the immune system

Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in the field of information fusion. Artificial immune systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has shown how AISs which use multi-level information sources as input data can be used to build effective algorithms for realtime computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs.     

对等网络（P2P）蠕虫模型分析与防御技术研究

P2P应用极大地满足了用户的需求,而P2P蠕虫是目前P2P应用面临的最严重的威胁之一。内容包括P2P蠕虫的特点、分类、传播模型、流量模型及其防御机制,并分析了各种模型的优缺点,对以后的研究方向进行了初步探讨。