Fuzzy-Pareto-dominance driven possibilistic model based planning of electrical distribution systems using multi-objective particle swarm optimization

This paper presents a fuzzy-Pareto dominance driven possibilistic model based planning of electrical distribution systems using multi-objective particle swarm optimization (MOPSO). This multi-objective planning model captures the possibilistic variations of the system loads using a fuzzy triangular number. The MOPSO based on the Pareto-optimality principle is used to obtain a set of non-dominated solutions representing different network structures under uncertainties in load demands and these non-dominated solutions are stored in an elite archive of limited size. Normally, choosing the candidate non-dominated solutions to be retained in the elite archive while maintaining the quality of the Pareto-approximation front as well as maintaining the diversity of solutions on this front is very much computationally demanding. In this paper, the principles of fuzzy Pareto-dominance are used to find out and rank the non-dominated solutions on the Pareto-approximation front. This ranking in turn is used to maintain the elite archive of limited size by discarding the lower ranked solutions. The two planning objectives are: (i) minimization of total installation and operational cost and (ii) minimization of risk factor. The risk factor is defined as a function of an index called contingency-load-loss index (CLLI), which captures the effect of load loss under contingencies, and the degree of network constraint violations. The minimization of the CLLI improves network reliability. The network variables that are optimized are: (i) number of feeders and their routes, and (ii) number and locations of sectionalizing switches. An MOPSO (developed by the authors), based on a novel technique for the selection and assignment of leaders/guides for efficient search of non-dominated solutions, is used as the optimization tool. The proposed planning approach is validated on a typical 100-node distribution system. Performance comparisons between the planning approaches with the possibilistic and deterministic load models are provided highlighting the relative merits and demerits. It is also verified that the proposed solution ranking scheme based on the fuzzy-Pareto dominance is very much better from both quality and computational burden point of view in comparison with the other well-known archive truncation techniques based on clustering and solution density measurement etc.     

Preference-based multi-objective evolutionary algorithms for power-aware application mapping on NoC platforms

Network-on-chip (NoC) are considered the next generation of communication infrastructure in embedded systems. In the platform-based design methodology, an application is implemented by a set of collaborative intellectual property (IP) blocks. The selection of the most suited set of IPs as well as their physical mapping onto the NoC infrastructure to implement efficiently the application at hand are two hard combinatorial problems that occur during the synthesis process of Noc-based embedded system implementation. In this paper, we propose an innovative preference-based multi-objective evolutionary methodology to perform the assignment and mapping stages. We use one of the well-known and efficient multi-objective evolutionary algorithms NSGA-II and microGA as a kernel. The optimization processes of assignment and mapping are both driven by the minimization of the required silicon area and imposed execution time of the application, considering that the decision maker’s preference is a pre-specified value of the overall power consumption of the implementation.     

基于目标的动态加权模糊控制

针对单输入、多目标控制系统，提出一种基于目标的动态加权模糊控制模型。该模型根据控制目标设计子模糊控制器，通过模糊推理得到的加权系数动态连接各个子目标模糊控制器，使多个子目标模糊控制器在控制过程中既相互竞争又相互协调。该模型用于吊车控制的仿真结果证明了其有效性。     

矿井瓦斯传感器自动调校技术及其研究进展

在简介矿井瓦斯传感器关键技术的基础上,阐述了气体传感器漂移的抑制,包括传感器零点、灵敏度和非线性的自动调校技术及其研究进展,介绍了几种最新技术,涉及遗传算法、小波分解以及用DSP的实现方法,尤其是基于神经网络的传感器非线性自动调校方法,用传感器的输出和待测物理量的实际数值训练神经网络,以得到非线性校正用的逆模型。RBF神经网络的收敛速度、分类能力和逼近能力都比较好,是目前的研究热点。     

回归测试中测试用例集缩减问题的研究

测试用例集缩减(Test Suite Minimization,TSM)问题作为回归测试的研究热点和难点,在满足对指定测试需求的覆盖前提下,通过识别并移除冗余测试用例来降低回归测试成本。对国内外已有的TSM研究成果进行综述。首先分别从源代码和模型两个角度出发,总结已有的TSM方法:从源代码角度出发,重点分析与总结传统TSM方法和考虑缺陷检测能力的TSM方法;从模型角度出发,重点分析与总结基于扩展有限状态自动机的TSM方法。然后对实证研究中采用的评测程序、评测指标和实证结论进行总结。随后总结了TSM方法在特定测试领域的应用,包括GUI应用测试、Web应用测试和缺陷定位等。最后展望了未来的可能发展趋势。     

混合遗传算法在铁路网车流分配中的应用

为减少铁车流在铁路网上的输送消耗以及均衡铁路线路的利用率,构建铁路网车流分配的多目标规划模型,针对传统智能算法在求解该问题时收敛性弱、易陷入局部最优的缺陷,提出设计自适应规则及退火策略的混合遗传算法进行求解仿真.采用特殊的染色体编码方式满足约束条件,对基本遗传算法的交叉、变异算子进行自适应改进,加强了算法的收敛性,引入模拟退火策略,提高了算法的邻域搜索能力.仿真结果表明,混合遗传算法在收敛代数以及求解性能上均有较大改进,可为车流分配问题提供有效解决方案.     

基于多点非均匀变异的多目标极值优化算法研究

多目标进化算法因其在解决含有多个矛盾目标函数的多目标优化问题中的强大处理能力,正受到越来越多的关注与研究。极值优化作为一种新型的进化算法,已在各种离散优化、连续优化测试函数以及工程优化问题中得到了较为成功的应用,但有关多目标EO算法的研究却十分有限。本文将采用Pareto优化的基本原理引入到极值优化算法中,提出一种求解连续多目标优化问题的基于多点非均匀变异的多目标极值优化算法。通过对六个国际公认的连续多目标优化测试函数的仿真实验结果表明：本文提出算法相比NSGA-II、 PAES、SPEA和SPEA2等经典多目标优化算法在收敛性和分布性方面均具有优势。     

基于特征的模型多目标优化分割算法

为了减少人为因素对分割结果的影响,提高计算精度和效率,保证分割方案的合理性,提出一种基于特征的模型分割算法,通过特征识别、可加工性分析,分割面获取及分割方案多目标优化4个主要步骤完成模型分割。由于分割过程中需要考虑多个目标函数,故将遗传算法多目标优化应用于分割过程中,通过对分割面和分割顺序的优化获得最优化分割结果,并以具体实例验证了算法的可行性。     

A robust underwater navigation method fusing data of gravity anomaly and magnetic anomaly

Navigation using geophysical information is often implemented on underwater vehicles to correct the position errors of inertial navigation system. In this paper, we fuse the information of gravity anomaly and magnetic anomaly by developing an improved elitism based multi-objective artificial bee colony algorithm. In the proposed algorithm, multi-objective matching strategy and sequence matching strategy are adopted for the purpose of reducing the possibility of mismatching. Specifically, the mean absolute difference (MAD) of gravity anomaly and the MAD of magnetic anomaly are selected as two matching objective. Single-point matching is changed to sequence matching by introducing affine transformation. To keep diversity without losing too much convergence speed, two elites chosen by different strategies are used to generate new food source. Finally, in order to avoid premature convergence, a new mechanism of scout bee phase is explored. The maximum capacity of external archive is regarded as the monitoring target. Simulations based on the actual gravity anomaly base map and the actual magnetic anomaly base map have been performed for the validation of the proposed algorithm.     

多目标0-1规划的蚂蚁优化算法

针对多目标0-1规划问题,设计一种新的求解方法--蚂蚁优化算法,并在计算机上予以实现,经与已有的算法相比较以及大量数据测试和验证,获得了较好的结果.