Response level crossing rate of a linear system excited by a partially specified Gaussian load process

The problem of determining the maximum mean response level crossing rate of a linear system driven by a partially specified Gaussian load process has been considered. The partial specification of the load is given only in terms of its total average energy. The critical input power spectral (PSD) function, which maximizes the mean response level crossing rate, is obtained. The critical input PSD turns out to be highly narrow-banded which fails to capture the erratic nature of the excitation. Consequently, the trade-off curve between the maximum mean response level crossing rate and the maximum disorder in the input process, quantified in terms of its entropy rate, has been generated. The method of Pareto optimization is used to tackle the conflicting objectives of the simultaneous maximization of the mean response level crossing rate and the input entropy rate. The non-linear multi-objective optimization has been carried out using a recently developed multi-criteria genetic algorithm scheme. Illustrative example of determining the critical input of an axially vibrating rod, excited by a partially specified stationary Gaussian load process, has been considered.     

The 2006 summary of scientific productivity of chemical engineers in Taiwan

In this report, the scientific productivity of Year 2006 of chemical engineering professionals in Taiwan was analyzed carefully with information retrieved from the database of the ISI Web of Science. The distributions of number of authors with x publication versus the number of publication x follows a general power-law trend, which coincidently comply with the Pareto distribution. The power-law indices found ranges from −1.64 to −2.22, which is consistent with values reported in the literature investigated for periods of 1907–1916 and 1969–2004.     

Handling multiple objectives with biogeography-based optimization

Biogeography-based optimization (BBO) is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization (BBMO) is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability effciently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II (NSGA-II) and archive-based micro genetic algorithm (AMGA) in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do.     

Generating Web Traffic Based on User Behavioral Model

Generating web traffic is of great importance to analyze performance of new designed network, test new equipment, and verify new protocols, etc.. However, most existing traffic generation systems tend to simulate the overall characteristics of network traffic, while neglecting of the behavior of the individual users. Nevertheless, in principle, the emerged characteristics of overall traffic originate from the aggregation of individual users＇ access behavior. In this paper, we propose an innovative web traffic generating method based on user browsing behavior. Our method simulates the real users＇ accessing behavior, and visits the real web servers. Then, we design and develop a web traffic generating system. Because our system accesses the real websites, it can produce almost the real network traffic. The test results show that the traffic generated by our system has characteristics of burstiness and self-similarity, which are widely found and characterized in many real networks. In addition, our system can better reflect real user＇s web browsing behavior.     

A multiobjective hybrid ant colony optimization approach applied to the assignment and scheduling problem

The assignment and scheduling problem is inherently multiobjective. It generally involves multiple conflicting objectives and large and highly complex search spaces. The problem allows the determination of an efficient allocation of a set of limited and shared resources to perform tasks, and an efficient arrangement scheme of a set of tasks over time, while fulfilling spatiotemporal constraints. The main objective is to minimize the project makespan as well as the total cost. Finding a good approximation set is the result of trade‐offs between diversity of solutions and convergence toward the Pareto‐optimal front. It is difficult to achieve such a balance with NP‐hard problems. In this respect, and in order to efficiently explore the search space, a hybrid bidirectional ant‐based approach is proposed in this paper, which is an improvement of a bi‐colony ant‐based approach. Its main characteristic is that it combines a solution construction developed for a more complicated problem with a Pareto‐guided local search engine.     

基于满意度和GA的区间系数多目标规划求解*

在缺乏先验知识的前提下,提出了约束条件的一种满意度,按照遗传算法求解多目标问题的步骤,求出非劣解集;然后根据适应度大小选出最优解;最后算例证明了该算法的有效性和可行性。     

基于多目标优化的多源组播网络编码的构造

在基于单目标优化构造网络编码的基础上,提出了基于多目标优化的网络编码的构造方法。把多源组播网络划分成多个单源组播网络,各单源组播网络的组播容量互相制约,为了使各单源组播网络的组播容量达到最大,采用粒子群优化算法进行子图划分,动态求解包含各子图组播容量的Pareto解集。用户可以优先考虑某个子图的组播容量,选择相应的解向量进行线性网络编码构造。仿真测试结果表明,本方法是可行的。     

基于约束满足的多目标双足机器人动态步态参数优化*

目前的步态优化算法仅仅实现了对单一目标的优化,把双足机器人步态优化看做是多目标优化问题,构建了衡量稳定性、能量消耗、步行速度三个目标评价函数。考虑到直接对多个目标加权求和的方法不能很好地处理多目标问题,提出一种新的基于约束满足的多目标步态参数优化算法,其思想是把基于惩罚函数的SPEA2(strength Pareto evolutionary algorithm2 )应用到多目标双足机器人动态步态参数优化问题上,规划出了同时满足这三个目标的动态优化步态。通过仿真实验表明了算法的有效性。     

大型复杂跨流域调水工程电站-水库-泵站群多目标优化调度

考虑调水量、发电最大和耗能最小3个目标,建立电站-水库-泵站群多目标优化调度模型。基于参数调整策略、邻域变异和加速策略,提出了求解多目标优化调度模型的综合改进布谷鸟新算法并求解模型,获得了发电、调水、泵站耗能多目标Pareto解集。以引汉济渭大型复杂跨流域调水工程为实例,将该模型与模拟调度模型和NSGA-Ⅱ算法多目标优化调度模型的结果进行比较,结果表明,该优化调度模型的发电、调水、耗能、弃水等各项指标合理,具有相对优势。