一种基于NSGA-II的网络规划资源配置优化处理方法

本文提供一种网络规划资源配置优化处理方法,利用第二代非支配排序遗传算法(NSGA-II)来解决网络规划资源配置优化多个目标通常彼此矛盾、难以同时优化的问题,结合通信网络规划技术特点,提供构建资源配置多目标优化模型方法、模型求解方法和资源分配实施示例,并对资源配置优化结论处理方法进行进一步的讨论。     

CREW: Cost and Reliability aware Eagle-Whale optimiser for service placement in Fog

Integration of Internet of Things (IoT) with industries revamps the traditional ways in which industries work. Fog computing extends Cloud services to the vicinity of end users. Fog reduces delays induced by communication with the distant clouds in IoT environments. The resource constrained nature of Fog computing nodes demands an efficient placement policy for deploying applications, or their services. The distributed and heterogeneous features of Fog environments deem it imperative to consider the reliability performance parameter in placement decisions to provide services without interruptions. Increasing reliability leads to an increase in the cost. In this article, we propose a service placement policy which addresses the conflicting criteria of service reliability and monetary cost. A multiobjective optimisation problem is formulated and a novel placement policy, Cost and Reliability-aware Eagle-Whale (CREW), is proposed to provide placement decisions ensuring timely service responses. Considering the exponentially large solution space, CREW adopts Eagle strategy based multi-Whale optimisation for taking placement decisions. We have considered real time microservice applications for validating our approaches, and CREW has been experimentally shown to outperform the existing popular multiobjective meta-heuristics such as NSGA-II and MOWOA based placement strategies.     

战术电子干扰行动的NSGA-II多目标决策建模与分析

从现代战争中的战术电子战行动指挥决策需求角度出发,以战场电子战战术行动为背景,抽象出战术电子战干扰行动的一般模式.建立了不仅考虑我方电子战装备对敌电子目标进行干扰时的任务分配还考虑装备与目标的有效干扰与匹配的电子干扰任务分配模型,并结合目标重要性优先、干扰距离优先和目标重要性和干扰距离兼顾三种优先干扰策略进一步加强模型实用性.运用非支配排序的遗传算法(Non-dominated Sorting Genetic Algorithm,NSGA-Ⅱ)对模型进行了算例分析和求解,并比较得出了三种优先干扰策略的优劣.通过对算例结果进行分析,得出了对战术电子战行动指挥员切实有效的指挥决策建议,表明了模型的有效性.     

NSGA-II算法的改进及其在应急管理中的应用

泥石流等突发自然灾害造成的人员伤亡和经济损失十分巨大,因此应急中心选址问题是应急救援方案中的核心环节。以救济物资效用、受灾区域满意度以及临时物资点数目为决策函数,建立多目标动态选址模型,提出了一种改进的非支配遗传排序算法（NSGA-II-TS）,该算法在精英策略上引入禁忌搜索的思想,从而实现了局部和全局搜索能力同时达到较优的结果,同时保留其解集的多样性和均匀性。数值算例结果表明该算法在物资效用、临时物资点个数、受灾区域满意度上比传统算法NSGA-II、MOEA/D更为合理。NSGA-II-TS算法在突发性灾害危机的应急管理以及其他保障体系建设问题中具有较高的应用价值。     

采用数量级阈值与二维信息排序策略的NSGA-II-DE算法

为提高算法NSGA-II-DE解决含有复杂Pareto解集优化问题的性能, 分析原NSGA-II中拥挤度计算公式和排挤机制的缺陷, 并以NSGA-II-DE算法为基本框架, 将传统拥挤度排序策略改为包含有角度信息与伪半径的二维信息排序策略. 在拥挤度排挤机制中加入数量级阈值的干预, 提出改进算法2D-Thr. 选取多样度、收敛度和分布度3个评价指标进行量化计算, 并与NSGA-II-DE、原NSGA-II、MACPSO进行比较. 仿真结果表明, 改进算法不仅有效继承了原算法优良的收敛性, 而且提高了Pareto前沿的分布度.

     

Two parameter tuned multi-objective evolutionary algorithms for a bi-objective vendor managed inventory model with trapezoidal fuzzy demand

This paper presents a bi-objective vendor managed inventory (BOVMI) model for a supply chain problem with a single vendor and multiple retailers, in which the demand is fuzzy and the vendor manages the retailers’ inventory in a central warehouse. The vendor confronts two constraints: number of orders and available budget. In this model, the fuzzy demand is formulated using trapezoidal fuzzy number (TrFN) where the centroid defuzzification method is employed to defuzzify fuzzy output functions. Minimizing both the total inventory cost and the warehouse space are the two objectives of the model. Since the proposed model is formulated into a bi-objective integer nonlinear programming (INLP) problem, the multi-objective evolutionary algorithm (MOEA) of non-dominated sorting genetic algorithm-II (NSGA-II) is developed to find Pareto front solutions. Besides, since there is no benchmark available in the literature to validate the solutions obtained, another MOEA, namely the non-dominated ranking genetic algorithms (NRGA), is developed to solve the problem as well. To improve the performances of both algorithms, their parameters are calibrated using the Taguchi method. Finally, conclusions are made and future research works are recommended.     

Causality on cross-sectional data: Stable specification search in constrained structural equation modeling

Causal modeling has long been an attractive topic for many researchers and in recent decades there has seen a surge in theoretical development and discovery algorithms. Generally discovery algorithms can be divided into two approaches: constraint-based and score-based. The constraint-based approach is able to detect common causes of the observed variables but the use of independence tests makes it less reliable. The score-based approach produces a result that is easier to interpret as it also measures the reliability of the inferred causal relationships, but it is unable to detect common confounders of the observed variables. A drawback of both score-based and constrained-based approaches is the inherent instability in structure estimation. With finite samples small changes in the data can lead to completely different optimal structures. The present work introduces a new hypothesis-free score-based causal discovery algorithm, called stable specification search, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Structure search is performed over structural equation models. Our approach uses exploratory search but allows incorporation of prior background knowledge. We validated our approach on one simulated data set, which we compare to the known ground truth, and two real-world data sets for chronic fatigue syndrome and attention deficit hyperactivity disorder, which we compare to earlier medical studies. The results on the simulated data set show significant improvement over alternative approaches and the results on the real-word data sets show consistency with the hypothesis driven models constructed by medical experts.     

Probabilistic and Multi-Objective Placement of D-STATCOM in Distribution Systems Considering Load Uncertainty

Advanced technologies such as distribution static compensator (D-STATCOM) are broadly deployed in distribution systems (DSs) to tackle different issues. Successful deployment of such technologies requires their adaption to stochastic nature of DS. This paper proposes a new method for allocation of D-STATCOM in DSs considering load uncertainty. Monte Carlo simulation (MCS) technique is utilized to cope with uncertain attribute of loads. To do so, output of MCS is considered as a deterministic case which is solved by backward-forward sweep power flow approach. The objective functions of the proposed model include total voltage deviation, total active losses, and voltage stability metrics. These metrics are combined in a multi-objective optimization formulation and solved by non-dominated sorting genetic algorithm-II (NSGA-II). Lastly, the best compromise solution is determined by Pareto front technique taking advantage of fuzzy decision making (FDM) engine. Comprehensive simulation studies are carried out on IEEE 33-bus standard distribution system as well as on Portuguese 94-bus real-world distribution system. Simulation studies confirm the accuracy of the proposed method in determining proper location of D-STATCOM in DSs by fulfilling the objectives.     

基于NSGA-Ⅱ方法的三峡水库汛末蓄水期多目标生态调度研究

为充分发挥三峡水库的经济效益和生态效益,建立了以三峡水电站发电量最大和下游河道适宜生态流量改变度最小为目标的水库优化调度模型,基于生态水文学法量化了下游河道适宜生态流量。为有效求解多目标优化问题,引入带有精英保留策略的非支配排序遗传算法(non-dominated sorting genetic algorithm,NSGA-II)作为调度模型的寻优算法。通过比较2015年调度期内三峡水库优化调度方案和现行调度方案的发电效益和生态效益表明:提高生态流量改变度0.63%可使发电量增加2.32%,降低0.58%的发电量将生态流量改变度降低8.86%。该算法避免了对多目标进行加权求解的盲目性,能获得多个兼顾两个目标且分布均匀、收敛性较好的优化调度方案,为决策者提供了多种可行的选择。研究成果为三峡水库蓄水优化调度提供理论依据和具体方案参考。     

基于改进的多目标进化算法的飞行控制系统优化*

针对在传统飞行控制系统控制器参数整定问题中单目标优化不能同时满足多个控制指标要求的缺点,提出了一种基于改进的NSGAII算法的多目标进化算法。在改进的NSGAII算法中,提出了改进的精英保留策略增强了算法收敛性;同时,使用改进的自适应模拟二进制（ASBX）算子提高了算法效率,提出使用改进的基于混沌序列的变异算子避免算法陷入局部最优解,以提高算法搜索精度。将改进的算法应用到飞机飞行控制系统设计中,仿真结果表明,该进化算法能够快速有效地进行飞行控制系统参数整定。