Binary particle swarm optimization with multiple evolutionary strategies

This paper introduces a novel variation of binary particle swarm optimization(BPSO) algorithm and a further extension to improve its performance.Firstly,mimicking the behaviors of some creatures group,multiple evolutionary strategies BPSO(MBPSO) is introduced which takes different evolutionary strategies for various particles according to their performances.Then,on the basis of MBPSO,a new strategy is discussed to improve the performance of the MBPSO(M2BPSO) which adopts the concept of the mutation operator aiming to overcome the premature convergence and slow convergent speed during the later stages of the optimization.The proposed two algorithms are tested on seven benchmark functions and their results are compared with those obtained by other methods.Experimental results show that our methods outperform the other algorithms.     

基于智能垃圾桶的垃圾分类动态收运路径优化问题研究

针对城市生活垃圾分类收运过程中存在的环境二次污染和垃圾产生量不确定性等问题,提出了一种基于智能垃圾桶的动态收运车辆路径优化方法。建立以最小化碳排放成本、燃油消耗成本、固定成本和车辆延迟到达惩罚成本为目标的动态车辆路径优化模型。采用滚动时域的方式将动态问题转换为一系列静态问题,并设计两阶段算法进行求解。首先采用粒子群算法对收运车辆路径进行规划,而后在每个时域末,综合考虑待清运垃圾桶的位置和垃圾量、垃圾收运车辆的位置和装载量以动态调整现有车辆路径。研究结果表明,相较于传统的静态收运方案,动态垃圾收运方案能够在降低车辆运输成本和碳排放成本的同时,显著降低由于清运不及时造成的环境二次污染的风险。     

LPNet: A DNN based latency prediction technique for application mapping in Network-on-Chip design

Analytical models used for latency estimation of Network-on-Chip (NoC) are not producing reliable accuracy. This makes these analytical models difficult to use in optimization of design space exploration. In this paper, we propose a learning based model using deep neural network (DNN) for latency predictions. Input features for DNN model are collected from analytical model as well as from Booksim simulator. Then this DNN model has been adopted in mapping optimization loop for predicting the best mapping of given application and NoC parameters combination. Our simulations show that using the proposed DNN model, prediction error is less than 12% for both synthetic and application specific traffic. More than 108 times speedup could be achieved using DPSO with DNN model compared to DPSO using Booksim simulator.     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.     

A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests

Combinatorial optimization problems are usually modeled in a static fashion. In this kind of problems, all data are known in advance, i.e. before the optimization process has started. However, in practice, many problems are dynamic, and change while the optimization is in progress. For example, in the dynamic vehicle routing problem (DVRP), new orders arrive when the working day plan is in progress. In this case, routes must be reconfigured dynamically while executing the current simulation. The DVRP is an extension of a conventional routing problem, its main interest being the connection to many real word applications (repair services, courier mail services, dial-a-ride services, etc.). In this article, a DVRP is examined, and solving methods based on particle swarm optimization and variable neighborhood search paradigms are proposed. The performance of both approaches is evaluated using a new set of benchmarks that we introduce here as well as existing benchmarks in the literature. Finally, we measure the behavior of both methods in terms of dynamic adaptation.     

A new hybrid approach for dynamic continuous optimization problems

A new hybrid approach for dynamic optimization problems with continuous search spaces is presented. The proposed approach hybridizes efficient features of the particle swarm optimization in tracking dynamic changes with a new evolutionary procedure. In the proposed dynamic hybrid PSO (DHPSO) algorithm, the swarm size is varied in a self-regulatory manner. Inspired from the microbial life, the particles can reproduce infants and the old ones die. The infants are especially reproduced by high potential particles and located near the local optimum points, using the quadratic interpolation method. The algorithm is adapted to perform in continuous search spaces, utilizing continuous movement of the particles and using Euclidian norm to define the neighborhood in the reproduction procedure. The performance of the new proposed approach is tested against various benchmark problems and compared with those of some other heuristic optimization algorithms. In this regard, different types of dynamic environments including periodic, linear and random changes are taken with different performance metrics such as real-time error, offline performance and offline error. The results indicate a desirable better efficiency of the new algorithm over the existing ones.     

一类线性时变群体系统稳定性的代数判据

针对在判断群体系统的稳定性时没有一般的方法和程序构造Lyapunov函数这个难点,利用矩阵范数,孤立子系统的矩阵指数函数与比较原理提出了一类线性时变群体系统平凡解一致稳定,所有解一致有界的充分条件。方便此类群体系统的稳定性分析,为研究其他群体系统稳定性的代数判据提供了理论基础。可以在此基础上,进一步研究一类非线性群体系统稳定性的代数判据。同时,还给出了具体算例,说明所提方法的正确性。此代数判据应用简便,灵活,适于实际应用。     

基于传感器网络节点配置优化仿真研究

研究无线传感网络节点配置覆盖优化问题。由于无线传感网络存在着热区问题,对网络的覆盖性能造成严重的影响,同时影响网络配置优化。为了有效的提高无线传感网络的覆盖率,提出了一种改进的粒子群算法优化无线网络节点覆盖。针对粒子群算法存在易陷入局部极值和早熟的缺陷,引入遗传算法中的交叉算子和变异算子,优化传感网络节点的混合粒子群算法,在严格确保无线传感器网络连通性的条件下,传感器节点配置数目达到要求的覆盖度,并进行仿真。仿真结果表明混合粒子群算法能快速收敛到更精确的解,使网络节点配置达到覆盖的优化要求。     

无人机空中加油控制设计与仿真

无人机空中加油控制精度问题,无人受油机模型的各种不确定性与来自外部的各种干扰归结为扰动,造成控制精度差。为解决上述问题,提出一种免疫粒子群优化算法的自抗扰无人机自主空中加油飞行控制律设计方法。利用自抗扰控制能够自动检测并补偿内部与外部干扰影响的特点,并利用扩张状态观测器进行估计与补偿,从而增强了所设计飞行控制律的鲁棒性,用免疫粒子群优化算法对自抗扰控制器参数进行了优化研究,以提高设计效率。仿真结果表明,所设计的自抗扰自主空中加油控制系统具有优良的控制性能与较高的控制精度,能够满足无人机自主空中加油的要求。     

涡扇发动机最优加速控制规律

关于涡扇发动机最优加速控问题,由于状态系统存在较强的非线性,控制性能差,改善发动机加速性,传统非线性规划算法求解过程中因采用罚函数处理约束条件而无法充分搜索控制参数的可行域。为提高系统性能,并充分挖掘发动机的加速特性,采用Sigma方法的多目标粒子群算法求解。可以在带限制因子的粒子群算法的基础上,利用粒子群算法的快速寻优能力和Sigma方法沿约束边界的充分搜索方法,求解发动机加速过程中控制参数,并进行仿真。结果证明,采用多目标粒子群算法优化后,加速时间缩短了约2.01s,结果表明改进方法是可行的,能在确保发动机安全工作的前提下,进一步提升了发动机的加速性能。