A Novel Compact Cat Swarm Optimization Based on Differential Method

ABSTRACT

A compact cat swarm optimization scheme (cCSO) is proposed in this paper, which is designed to solve application domains plagued with limited memory and less-computation power, as a member of cat swarm optimization algorithms (CSO), it composes of two sub-modes, i.e., tracing and seeking modes, so it keeps the same search logic of CSO. On the other hand, cCSO inherits the main feature of compact algorithms, a normal probabilistic model is used to represent the population of solutions instead of processing an actual population, which ensures the cCSO to have the modest memory requirement. The updating vector for the probabilistic model provides a clear moving direction for cats in next step. A cat without historical position and velocity is applied in the algorithm. When the cat is in seeking mode, it employs a differential operator to update the cat’s position, which makes it possible for the cat to have multiple searching directions. Experimental results show that cCSO has pretty performance compared with respect to some population-based testing benchmarks. And it also shows superior performance in convergence rate to some compact optimization algorithms. The case study of gray image segmentation proves that it suits for solving the optimization problem by limited hardware.     

Robust scheduling for large scale evacuation planning

It is crucial to develop appropriate strategies to reduce the evacuation time in a disaster situation. The negative impact of large scale disasters can be mitigated by proactive and efficient (time optimal) evacuation planning. The present study aims to develop strategies for public transit-based evacuation for better control and reduced congestion. Mathematical models are formulated for both strategic and operational aspects of evacuation planning to result in efficient, optimal evacuation. The study also presents methods to manage the external environment uncertainties, in particular, evacuation demand uncertainty, by providing robust solutions. To test the efficacy of the models, a case study for a radiological accident in a nuclear plant in India is presented. The results of the case study demonstrate that the models can provide live, efficient and robust results during actual emergencies in acceptable time.     

供应链视角下餐饮O2O模式的配送路径优化

O2O最后一公里配送往往需要综合考虑时间和成本的关系,研究平台对订单的分配和路径优化对提升O2O供应链的整体效率和产业链升级具有十分重要的意义。从餐饮商家和O2O平台两个主要参与者出发对物流配送系统进行研究,提出了适合于O2O餐饮供应链的配送网络,并考虑道路阻塞、最大里程数等约束,搭建了上层模型最短时间、下层模型最低成本的双层规划模型,进而通过NSGA-Ⅱ算法求得可行解,最后验证了算法的可行性,为进一步研究O2O配送提供了新的解决思路。     

石油海运领域运筹优化方法应用研究综述

为研究运筹方法在石油海运领域的应用及前景,检索1980年至今的国内外相关文献,系统归纳提出石油海上运输优化问题的场景分类:海上运输优化、在港作业调度优化以及油船驳运,分析并对比了各研究的模型差异及算法优劣,最后提出了全局优化调度、根据实际添加特殊约束和设置变量、利用规划约束及人工智能等方式弥补线性规划求解的不足等研究建议。     

航班计划优化研究概述

从航班计划优化的不同时间阶段分析,可以将航班计划优化分为航班计划静态编排优化、基于航班延误预测的航班计划动态反馈优化和基于机场协同决策(A-CDM)的航班计划动态调整角度三类;进而从航班时刻、机型指派、航班频率等编制环节分析了航班计划静态编排优化;随后利用延误波及预测与数据挖掘预测的优化方法分析了基于航班延误预测的航班计划动态反馈优化的相关研究。最后,根据航班计划优化复杂性分析,给出了航班计划优化的发展趋势和未来研究方向。     

无穷范数松弛的低复杂度超奈奎斯特检测

超奈奎斯特(Faster-than-Nyquist,FTN)速率传输可以有效提高频谱效率，但这种非正交传输方式引入的严重码间串扰相应提高了接收端的处理难度。针对该问题，设计了一种基于循环成块传输的低复杂度检测算法。最优检测被建模为无约束的二元二次规划(Boolean Quadratic Program,BQP)问题，为了求解该NP-hard问题，采用无穷范数约束松弛原问题的非凸可行解集，并基于次梯度下降法提出松弛问题的有效优化算法。数值仿真结果表明，所提算法在误比特率(Bit Error Rate,BER)性能上优于频域均衡，且在可接受的性能损失范围内算法执行效率远高于理论最优的最大似然序列估计(Maximum Likelihood Sequence Estimation,MLSE)。     

海南岛休闲农业布局现状的实证分析

[目的]全面分析海南岛休闲农业布局现状,为海南岛休闲农业科学合理规划和均衡协调发展提供决策参考。[方法]文章利用海南岛259家休闲农业企业的地理数据,采用比较、缓冲、密度分析工具,从数量、类型、密度、距离衰减方面实证分析海南岛休闲农业的布局特征,并提出相应的优化对策。[结果](1)海南岛东部、中部和西部休闲农业数量分布比重分别为51%、29%和20%,其中国家级示范点数量分布比重分别为83%、17%和0%;(2)休闲农业主要类型有休闲农庄、观光农园、市民农园和农家乐,除休闲农庄在东部、中部、西部分布较均匀外,其他类型分布较不均衡;(3)距离市县中心越远,休闲农业分布密度越小,95%的休闲农业分布在距离市县中心30km以内,距离市县中心10~15km是休闲农业的高峰集聚区;(4)海南岛休闲农业已在海口、保亭、三亚、澄迈、琼中、琼海、定安、儋州形成8个高度集聚带。[结论]海南岛休闲农业无论在数量、类型和密度上都是东部地区最多,中部次之,西部最少。应积极推进具有农业发展优势的地区,因地制宜拓展产业休闲功能;休闲农业产品类型单一的地区,深度挖掘特色资源和文化内涵;加强休闲农业点与旅游景区、生活服务中心之间的跨区域联系,形成和谐发展的有机旅游整体,使休闲农业真正成为农业转型、农民增收的引擎。     

Are the interdependence characteristics of the US and Canadian energy equity sectors nonlinear and asymmetric?

Nonlinear, symmetric, and asymmetric dependence characteristics in energy equity sectors matter to portfolio investors and risk managers because of the risks and diversification opportunities they entail. Specifically, nonlinear dependence dynamics between assets are harder to predict, monitor, and manage, and can make investment positions go wrong unexpectedly. In this paper, we investigate whether the dependence dynamics of US and Canadian large-capitalized energy equity portfolios are nonlinear, symmetric, or asymmetric. We draw our results by implementing a robust copula approach based on time-varying parameter copulas and vine copula methods. Both time varying parameter and vine-copula methods indicate that the Canadian energy sector portfolio is driven by nonlinear negative tail asymmetric dependence during the global financial crisis and when the full sample period is employed. On the other hand, it displays nonlinear symmetric dependence during the oil price crisis, implying the need for close monitoring and rebalancing and a more continuous assessment of long investment positions. The US energy sector portfolio is driven by positive tail asymmetric dependence, and by symmetric dependence dynamics during crisis and non-crisis periods.     

政府投资工程总承包项目设计优化认定与收益归属研究

工程总承包模式以设计与施工的高度融合,日益在建筑业受到青睐。但由于政府投资项目中存在有关结余资金上缴国库等规定,容易导致政府投资工程总承包项目下设计优化产生的成果是进行分成或作为结余资金上缴界定不清。鉴于此,本文通过政策文件,并结合政府投资项目特点,分析政府投资工程总承包项目设计优化的情形认定以及在不同结算依据和合同计价方式下设计优化收益归属的界定,为有效激励总承包单位积极进行设计优化奠定基础。     

Sharing the value‐at‐risk under distributional ambiguity

This paper considers the problem of risk sharing, where a coalition of homogeneous agents, each bearing a random cost, aggregates their costs, and shares the value‐at‐risk of such a risky position. Due to limited distributional information in practice, the joint distribution of agents' random costs is difficult to acquire. The coalition, being aware of the distributional ambiguity, thus evaluates the worst‐case value‐at‐risk within a commonly agreed ambiguity set of the possible joint distributions. Through the lens of cooperative game theory, we show that this coalitional worst‐case value‐at‐risk is subadditive for the popular ambiguity sets in the distributionally robust optimization literature that are based on (i) convex moments or (ii) Wasserstein distance to some reference distributions. In addition, we propose easy‐to‐compute core allocation schemes to share the worst‐case value‐at‐risk. Our results can be readily extended to sharing the worst‐case conditional value‐at‐risk under distributional ambiguity.