A FLEXIBLE OBJECTIVE-CONSTRAINT APPROACH AND A NEW ALGORITHM FOR CONSTRUCTING THE PARETO FRONT OF MULTIOBJECTIVE OPTIMIZATION PROBLEMS

In this article, a novel scalarization technique, called the improved objective-constraint approach, is introduced to find efficient solutions of a given multiobjective programming problem. The presented scalarized problem extends the objective-constraint problem. It is demonstrated that how adding variables to the scalarized problem, can lead to find conditions for (weakly, properly) Pareto optimal solutions. Applying the obtained necessary and sufficient conditions, two algorithms for generating the Pareto front approximation of bi-objective and three-objective programming problems are designed. These algorithms are easy to implement and can achieve an even approximation of (weakly, properly) Pareto optimal solutions. These algorithms can be generalized for optimization problems with more than three criterion functions, too. The effectiveness and capability of the algorithms are demonstrated in test problems.

Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier

The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective optimization.As seen,a variety of decision variable groupingalgorithms have been proposed.However,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto frontier.To solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is proposed.This algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision variables.In the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the algorithm.Evaluation validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.

Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

More devices in the Intelligent Internet of Things(AIoT)result in an increased number of tasks that require low latency and real-time responsiveness,leading to an increased demand for computational resources.Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension.However,the effective allocation of resources for task execution within fog environments,characterized by limitations and heterogeneity in computational resources,remains a formidable challenge.To tackle this challenge,in this study,we integrate fog computing and cloud computing.We begin by establishing a fog-cloud environment framework,followed by the formulation of a mathematical model for task scheduling.Lastly,we introduce an enhanced hybrid Equilibrium Optimizer(EHEO)tailored for AIoT task scheduling.The overarching objective is to decrease both the makespan and energy consumption of the fog-cloud system while accounting for task deadlines.The proposed EHEO method undergoes a thorough evaluation against multiple benchmark algorithms,encompassing metrics likemakespan,total energy consumption,success rate,and average waiting time.Comprehensive experimental results unequivocally demonstrate the superior performance of EHEO across all assessed metrics.Notably,in the most favorable conditions,EHEO significantly diminishes both the makespan and energy consumption by approximately 50%and 35.5%,respectively,compared to the secondbest performing approach,which affirms its efficacy in advancing the efficiency of AIoT task scheduling within fog-cloud networks.

Evaluation of the effectiveness of preventive nursing measures for pressure injuries in patients in the neurology intensive care unit

BACKGROUND Patients in neurology intensive care units(ICU)are prone to pressure injuries(PU)due to factors such as severe illness,long-term bed rest,and physiological dysfunction.PU not only causes pain and complications to patients,but also increases medical burden,prolongs hospitalization time,and affects the recovery process.AIM To evaluate and optimize the effectiveness of pressure injury prevention nursing measures in neurology ICU patients.METHODS A retrospective study was conducted,and 60 patients who were admitted to the ICU of the Department of Neurology were selected and divided into an observation group and a control group according to the order of admission,with 30 people in each group.The observation group implemented pressure injury prevention and nursing measures,while the control group adopted routine care.RESULTS Comparison between observation and control groups following pressure injury prevention nursing intervention revealed significantly lower incidence rates in the observation group compared to the control group at 48 h(8.3%vs 26.7%),7 d(16.7%vs 43.3%),and 14 d(20.0%vs 50.0%).This suggests a substantial reduction in pressure injury incidence in the observation group,with the gap widening over time.Additionally,patients in the observation group exhibited quicker recovery,with a shorter average time to get out of bed(48 h vs 72 h)and a shorter average length of stay(12 d vs 15 d)compared to the control group.Furthermore,post-intervention,patients in the observation group reported significantly improved quality of life scores,including higher scores in body satisfaction,feeling and function,and comfort(both psychological and physiological),indicating enhanced overall well-being and comfort following the implementation of pressure injury prevention nursing measures.CONCLUSION Implementing pressure injury preventive care measures for neurology ICU patients will have better results.

Research on Optimal Design of Campus Space Based on POE Concept: A Case Study of Anhui Xinhua College

Through the analysis of the overall landscape,revetments and roads and plant landscape,10 evaluation factors were determined.The comprehensive evaluation model for the campus space of Anhui Xinhua University was constructed by analytic hierarchy process(AHP).The results showed that revetment safety,road convenience,plant disease resistance and campus activity space were important factors affecting the spatial form planning of campus.Through the comparative analysis of the collected data,optimization suggestions were put forward to provide a basis for the establishment of“people-oriented”campus open space system.

ROS2 Real-time Performance Optimization and Evaluation

Real-time interaction with uncertain and dynamic environments is essential for robotic systems to achieve functions such as visual perception,force interaction,spatial obstacle avoidance,and motion planning.To ensure the reliability and determinism of system execution,a flexible real-time control system architecture and interaction algorithm are required.The ROS framework was designed to improve the reusability of robotic software development by providing a distributed structure,hardware abstraction,message-passing mechanism,and application prototypes.Rich ecosystems for robotic development have been built around ROS1 and ROS2 architectures based on the Linux system.However,because of the fairness scheduling principle of the default Linux system design and the complexity of the kernel,the system does not have real-time computing.To achieve a balance between real-time and non-real-time computing,this paper uses the transmission mechanism of ROS2,combines it with the scheduling mechanism of the Linux operating system,and uses Preempt_RT to enhance the real-time computing of ROS1 and ROS2.The real-time performance evaluation of ROS1 and ROS2 is conducted from multiple perspectives,including throughput,transmission mode,QoS service quality,frequency,number of subscription nodes and EtherCAT master.This paper makes two significant contributions:firstly,it employs Preempt_RT to optimize the native ROS2 system,effectively enhancing the real-time performance of native ROS2 message transmission;secondly,it conducts a comprehensive evaluation of the real-time performance of both native and optimized ROS2 systems.This comparison elucidates the benefits of the optimized ROS2 architecture regarding real-time performance,with results vividly demonstrated through illustrative figures.

A Study of Optimum Switching Problem for Production Systems Considering Efficiency, Delivery Time and Green Evaluation

This paper aims to derive the optimal switching strategy for production system considering efficiency, delivery time and green evaluation. Nowadays more and more manufacturing and logistics systems not only pursue better work efficiency, but also focus on green energy evaluation issues. Cost reduction and shortening of delivery time are always important management issues in pursuit of efficiency and optimization of the entire production system because of global production competition. In a market situation where customer needs change in various ways, in particular, due to inadequate quality, changes in the local environment, natural disasters and so on. Therefore, prompt planning of management measures such as switching work processes and changing production methods has become an important issue. On the other hand, since the Paris Agreement came into effect, the construction of an environment-friendly production system has been required as an approach to environmental problems such as global warming. In this paper, we propose an optimum switching model of production systems considering efficiency, delivery time and green evaluation using a green evaluation index (GEC: Green Energy Coefficient). We also discuss the optimal switching strategy by numerical observation.

Pareto-Optimal Reinsurance Based on TVaR Premium Principle and Vajda Condition

Reinsurance is an effective risk management tool for insurers to stabilize their profitability. In a typical reinsurance treaty, an insurer cedes part of the loss to a reinsurer. As the insurer faces an increasing number of total losses in the insurance market, the insurer might expect the reinsurer to bear an increasing proportion of the total loss, that is the insurer might expect the reinsurer to pay an increasing proportion of the total claim amount when he faces an increasing number of total claims in the insurance market. Motivated by this, we study the optimal reinsurance problem under the Vajda condition. To prevent moral hazard and reflect the spirit of reinsurance, we assume that the retained loss function is increasing and the ceded loss function satisfies the Vajda condition. We derive the explicit expression of the optimal reinsurance under the TVaR risk measure and TVaR premium principle from the perspective of both an insurer and a reinsurer. Our results show that the explicit expression of the optimal reinsurance is in the form of two or three interconnected line segments. Under an additional mild constraint, we get the optimal parameters and find the optimal reinsurance strategy is full reinsurance, no reinsurance, stop loss reinsurance, or quota-share reinsurance. Finally, we gave an example to analyze the impact of the weighting factor on optimal reinsurance.

Reliability Evaluation Optimal Selection Model of Component-Based System

If the components in a component-based software system come from different sources, the characteristics of the components may be different. Therefore, evaluating the reliability of a component-based system with a fixed model for all components will not be reasonable. To solve this problem, this paper combines a single reliability growth model with an architecture-based reliability model, and proposes an optimal selecting approach. First, the most appropriate model of each component is selected according to the historical reliability data of the component, so that the evaluation deviation is the smallest. Then, system reliability is evaluated according to both the relationships among components and the using frequency of each component. As the approach takes into account the historical data and the using frequency of each component, the evaluation and prediction results are more accurate than those of using a single model.

Study on Government Performance Evaluation and Optimal Countermeasures of Sewage Treatment in Beijing City

In this paper,from the three dimensions of development quality,sustainable development and public satisfaction,the index system of government performance evaluation of wastewater treatment in Beijing City was constructed by conversion of critical success factor method.Using comprehensive evaluation method based on entropy weight method and principal component analysis method,the sewage treatment effect of Beijing municipal government from 2010 to 2017 was quantified,and the government s sewage treatment work was comprehensively evaluated.The evaluation results showed that the government has achieved remarkable results in sewage treatment,but it should continue to optimize the countermeasures in promoting the transformation and upgrading of economic structure,strengthening the management of urban sewage treatment and strict management system of water environment.

考虑Pareto最优的列车运行图与维修天窗协调优化

列车运行图与维修天窗之间的冲突始终无法避免,且维修天窗开设时间的长短显著影响列车总运行时间。针对此问题,综合考虑维修天窗对列车运行造成的限速约束、车站到发线数量约束等,建立列车总运行时间最小,以及维修天窗实际开设时长与理想时长总偏差最小的双目标混合整数规划模型;对困难约束设置中间辅助变量将模型线性化以提高求解效率,并设计约束转换算法求解双目标模型的Pareto最优;微观化处理铁路线,将站内资源和站间资源细化为一系列行车资源单元,得到更加符合实际旅客运输需求的运行图。以某地区铁路线夜间开行列车及维修天窗开设计划为研究背景,调用商业软件求解双目标函数模型的Pareto最优,并对双目标模型的最小支配解和最优支配解进行对比分析;针对最优支配解下的列车进入、离开行车资源单元的时间、停站作业时间及维修天窗的开始时间及开设时长,绘制列车运行图。求解结果表明:模型在满足维修天窗最小开设时长的同时,能够兼顾列车运行总时间最小和维修天窗开设时长更充裕。基于最优支配解绘制的列车运行图表明:微观路网下的列车运行时刻表优化结果更符合实际旅客运输生产作业需要。研究结果可为铁路运营管理部门进一步优化列车运行图编制与维修天窗开设提供参考。

基于Pareto Optimality的PPP项目三大主参与方效用探讨

在PPP项目的众多参与方中,选取项目发起人、SPC和贷款银行三个最主要的参与方,通过分析项目发起人与SPC、SPC与贷款银行和贷款银行与项目发起人之间的内在联系,在Pareto Optimality理论分析的基础上,得到三者之间两两优势互补的埃奇沃思方框图,并根据瓦尔拉斯均衡理论得出PPP项目能够实现帕累托最优,实现项目效用的最大化,最后拟画出PPP项目三大主参与方的效用可能性曲线。

Machine tool selection based on fuzzy evaluation and optimization of cutting parameters

The paper analyzes the factors influencing machine tool selection. By using fuzzy mathematics theory, we establish a theorietical model for optimal machine tool selection considering geometric features, clamping size, machining range, machining precision and surface roughness. By means of fuzzy comprehensive evaluation method, the membership degree of machine tool selection and the largest comprehensive evaluation index are determined. Then the reasonably automatic selection of machine tool is realized in the generative computer aided process planning （CAPP） system. Finally, the finite element model based on ABAQUS is established and the cutting process of machine tool is simulated. According to the theoretical and empirical cutting parameters and the curve of surface residual stress, the optimal cutting parameters can be determined.

基于Pareto-GA多目标的企业管理系统优化研究——以某造纸厂为例

传统造纸厂管理优化通常只针对单一目标,忽略了质量和安全等重要方面。因此,提出了一种面向造纸厂的多个目标优化方法,并结合Pareto排序以及遗传算法搜索机制改进的Pareto-遗传算法作为求解方法,以实现对造纸厂监管系统的优化设计。研究结果显示,使用Schaffer's F6 Function进行测试时,改进的Pareto-遗传算法在72次迭代后达到最大适应度值0.93,优于其他两种算法。进一步将工期、成本、质量和安全多目标问题分解为两个子问题,成功获得3组Pareto最优解,为管理者提供不同需求下的优化方案。同时,提出的造纸厂管理系统优化设计方案能够提升造纸厂管理的效率和安全性,具有重要的理论价值和实际应用前景。

Evaluation of the submarine debris-flow hazard risks to planned subsea pipeline systems: a case study in the Qiongdongnan Basin, South China Sea

The ever-increasing deepwater oil and gas development in the Qiongdongnan Basin,South China Sea has initiated the need to evaluate submarine debris-flow hazard risks to seafloor infrastructures.This paper presents a case study on evaluating the debris-flow hazard risks to the planned pipeline systems in this region.We used a numerical model to perform simulations to support this quantitative evaluation.First,one relict failure interpreted across the development site was simulated.The back-analysis modeling was used to validate the applicability of the rheological parameters.Then,this model was applied to forecast the runout behaviors of future debris flows originating from the unstable upslope regions considered to be the most critical to the pipeline systems surrounding the Manifolds A and B.The model results showed that the potential debris-flow hazard risks rely on the location of structures and the selection of rheological parameters.For the Manifold B and connected pipeline systems,because of their remote distances away from unstable canyon flanks,the potential debris flows impose few risks.However,the pipeline systems around the Manifold A are exposed to significant hazard risks from future debris flows with selected rheological parameters.These results are beneficial for the design of a more resilient pipeline route in consideration of future debris-flow hazard risks.

Accurate Evaluation of Free-form Surface Profile Error Based on Quasi Particle Swarm Optimization Algorithm and Surface Subdivision

Although significant progress has been made in precision machining of free-form surfaces recently, inspection of such surfaces remains a difficult problem. In order to solve the problem that no specific standards for the verification of free-form surface profile are available, the profile parameters of free-form surface are proposed by referring to ISO standards regarding form tolerances and considering its complexity and non-rotational symmetry. Non-uniform rational basis spline（NURBS） for describing free-form surface is formulated. Crucial issues in surface inspection and profile error verification are localization between the design coordinate system（DCS） and measurement coordinate system（MCS） for searching the closest points on the design model corresponding to measured points. A quasi particle swarm optimization（QPSO） is proposed to search the transformation parameters to implement localization between DCS and MCS. Surface subdivide method which does the searching in a recursively reduced range of the parameters u and v of the NURBS design model is developed to find the closest points. In order to verify the effectiveness of the proposed methods, the design model is generated by NURBS and the measurement data of simulation example are generated by transforming the design model to arbitrary position and orientation, and the parts are machined based on the design model and are measured on CMM. The profile errors of simulation example and actual parts are calculated by the proposed method. The results verify that the evaluation precision of freeform surface profile error by the proposed method is higher 10%-22% than that by CMM software. The proposed method deals with the hard problem that it has a lower precision in profile error evaluation of free-form surface.

Teaching evaluation on a WebGIS course based on dynamic self-adaptive teaching–learning-based optimization

Teaching evaluation on a WebGIS course is a multi-objective nonlinear high-dimensional NP-hard problem. The index system for the teaching evaluation of a WebGIS course, including teacher- and student-oriented sub-systems, is first established and used for questionnaires from 2013 to 2017. The multi-objective nonlinear high-dimensional evaluation model is constructed and then solved via dynamic self-adaptive teaching–learning-based optimization (DSATLBO). DSATLBO is based on teaching–learning-based optimization with five improvements: dynamic nonlinear self-adaptive teaching factor, extracurricular tutorship factor, dynamic self-adaptive learning factor, multi-way learning factor, and non-dominated sorting factor. WebGIS teaching performance is fully evaluated based on questionnaires and DSATLBO. Optimal weights and weighted scores from DSATLBO are compared with those from the non-dominated sorting genetic algorithm-II using the Pareto front, coverage to two sets, and spacing of the non-dominated solution sets to validate the performance of DSATLBO. The results show that DSATLBO can be uniformly distributed along the Pareto front. Therefore, DSATLBO can efficiently and feasibly solve the multi-objective nonlinear high-dimensional teaching evaluation model of a WebGIS course. The proposed teaching evaluation method can help reflecting the quality of all aspects of classroom teaching and guide the professional development of students.

海上风电半潜式基础初步选型 Pareto⁃Optimal 评价

针对半潜漂浮式风电基础初步选型,采用Pareto⁃Optimal评价方法对不同吃水、平台立柱直径、立柱间距和垂荡板直径四个参数的不同组合进行分析比较。基于浮体动力学频域计算方法,采用我国阳江某海域极限波浪条件计算得到叶轮中心水平加速度,同时考虑完整稳性的计算结果。对比分析表明平台吃水和立柱直径宜选择适中的取值,较大的排水量和立柱总体积并不会显著减小叶轮中心水平加速度。垂荡板对于改善平台整体性能是较为敏感的,垂荡板与立柱的直径比存在一定的最佳范围。平台立柱间距是影响平台运动性能最大的因素,增大立柱间距可以有效地降低叶轮中心水平加速度,但立柱间距的增大对立柱间的撑杆结构强度以及平台整体的建造和下水提出了较大的挑战。

Optimal setting and placement of FACTS devices using strength Pareto multi-objective evolutionary algorithm

This work proposes a novel approach for multi-type optimal placement of flexible AC transmission system(FACTS) devices so as to optimize multi-objective voltage stability problem. The current study discusses a way for locating and setting of thyristor controlled series capacitor(TCSC) and static var compensator(SVC) using the multi-objective optimization approach named strength pareto multi-objective evolutionary algorithm(SPMOEA). Maximization of the static voltage stability margin(SVSM) and minimizations of real power losses(RPL) and load voltage deviation(LVD) are taken as the goals or three objective functions, when optimally locating multi-type FACTS devices. The performance and effectiveness of the proposed approach has been validated by the simulation results of the IEEE 30-bus and IEEE 118-bus test systems. The proposed approach is compared with non-dominated sorting particle swarm optimization(NSPSO) algorithm. This comparison confirms the usefulness of the multi-objective proposed technique that makes it promising for determination of combinatorial problems of FACTS devices location and setting in large scale power systems.

Bi-Objective Optimization: A Pareto Method with Analytical Solutions

Multiple objectives to be optimized simultaneously are prevalent in real-life problems. This paper develops a new Pareto Method for bi-objective optimization which yields analytical solutions. The Pareto optimal front is obtained in closed-form, enabling the derivation of various solutions in a convenient and efficient way. The advantage of analytical solution is the possibility of deriving accurate, exact and well-understood solutions, which is especially useful for policy analysis. An extension of the method to include multiple objectives is provided with the objectives being classified into two types. Such an extension expands the applicability of the developed techniques.