Role of Examples and Interpretation of Results in Developing Multi-Objective Optimization Techniques

The paper evaluates the suitability of examples used in developing averaging techniques of multi-objective optimization (MOO). Most of the examples used for proposing these techniques were not suitable. The results of these examples have also not been interpreted correctly. An appropriate example has also been solved with existing and improved averaging techniques of multi-objective optimization.

Multi-Objective Optimization of Traffic Signal Timing at Typical Junctions Based on Genetic Algorithms

With the rapid development of urban road traffic and the increasing number of vehicles,how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities.Therefore,in this paper,a nonlinear multi-objective optimization model of urban intersection signal timing based on a Genetic Algorithm was constructed.Specifically,a typical urban intersection was selected as the research object,and drivers’acceleration habits were taken into account.What’s more,the shortest average delay time,the least average number of stops,and the maximum capacity of the intersection were regarded as the optimization objectives.The optimization results show that compared with the Webster method when the vehicle speed is 60 km/h and the acceleration is 2.5 m/s^(2),the signal intersection timing scheme based on the proposed Genetic Algorithm multi-objective optimization reduces the intersection signal cycle time by 14.6%,the average vehicle delay time by 12.9%,the capacity by 16.2%,and the average number of vehicles stop by 0.4%.To verify the simulation results,the authors imported the optimized timing scheme into the constructed Simulation of the Urban Mobility model.The experimental results show that the authors optimized timing scheme is superior to Webster’s in terms of vehicle average loss time reduction,carbon monoxide emission,particulate matter emission,and vehicle fuel consumption.The research in this paper provides a basis for Genetic algorithms in traffic signal control.

Multi-objective optimization scheduling for new energy power system considering energy storage participation

For the low utilization rate of photovoltaic power generation,taking a new energy power system constisting of concentrating solar power(CSP),photovoltaic power(PP)and battery energy storage system as an example,a multi-objective optimization scheduling strategy considering energy storage participation is proposed.Firstly,the new energy power system model is established,and the PP scenario generation and reduction frame based on the autoregressive moving average model and Kantorovich-distance is proposed.Then,based on the optimization goal of the system operation cost minimization and the PP output power consumption maximization,the multi-objective optimization scheduling model is established.Finally,the simulation results show that introducing energy storage into the system can effectively reduce the system operation cost and improve the utilization efficiency of PP.

Duality in Solving Multi-Objective Optimization (MOO) Problems

Multi-Objective Optimization (MOO) techniques often achieve the combination of both maximization and minimization objectives. The study suggests scalarizing the multi-objective functions simpler using duality. An example of four objective functions has been solved using duality with satisfactory results.

Earth-moon Trajectory Optimization Using Solar Electric Propulsion

The optimization of the Earth-moon trajectory using solar electric propulsion is presented. A feasible method is proposed to optimize the transfer trajectory starting from a low Earth circular orbit （500 km altitude） to a low lunar circular orbit （200 km altitude）. Due to the use of low-thrust solar electric propulsion, the entire transfer trajectory consists of hundreds or even thousands of orbital revolutions around the Earth and the moon. The Earth-orbit ascending （from low Earth orbit to high Earth orbit） and lunar descending （from high lunar orbit to low lunar orbit） trajectories in the presence of J2 perturbations and shadowing effect are computed by an analytic orbital averaging technique. A direct/indirect method is used to optimize the control steering for the trans-lunar trajectory segment, a segment from a high Earth orbit to a high lunar orbit, with a fixed thrust-coast-thrust engine sequence. For the trans-lunar trajectory segment, the equations of motion are expressed in the inertial coordinates about the Earth and the moon using a set of nonsingular equinoctial elements inclusive of the gravitational forces of the sun, the Earth, and the moon. By way of the analytic orbital averaging technique and the direct/indirect method, the Earth-moon transfer problem is converted to a parameter optimization problem, and the entire transfer trajectory is formulated and optimized in the form of a single nonlinear optimization problem with a small number of variables and constraints. Finally, an example of an Earth-moon transfer trajectory using solar electric propulsion is demonstrated.

Location of Tibetan earthquakes──a nonlinear approach by a simplex optimized technique

An advanced earthquake location technique presented by Prugger and Gendzwill (1988) was introduced in this paper. Its characteristics are: 1) adopting the difference between the mean value by observed arrival times and the mean value by calculated travel times as the original reference time of event to calculate the traveltime residuals, thus resulting in the 'true' minimum of travel-time residuals; 2) choosing the L1 norm statistic of the residuals that is more suitable to earthquake location; 3) using a simplex optimized algorithm to search for the minimum residual value directly and iteratively, thus it does not require derivative calculations and avoids matrix inversions, it can be used for any velocity structures and different network systems and can solve out hypocentral parameters (λ, ,h) rapidly and exactly; 4) original time is further derived alone, so the trade-off between focal depth and original time is avoided. All these prominent features make us obtain more accurate Tibetan earthquake locations in the rare network condition by using this method. In this paper, we examined these schemes for our mobile and permanent networks in Tibet with artificial data sets,then using these methods, we determined the hypocentral parameters of partial events observed in the field work period of this project from July 1991 to September 1991 and the seven problematic earthquakes during 1989 - 1990. The hypocentral location errors may be estimated to be less than 3. 6 km approximately. The events with focal depth more than 40 km seem to be distributed in parallel to Qinghai-Sichuan-Yunnan arc structural zone.

Metamodel-Based Multi-Objective Reliable Optimization for Front Structure of Electric Vehicle

In this paper,a multi-objective reliable optimization(MORO)procedure for the front body of an electric vehicle is proposed and compared with determinate multi-objective optimization(DMOO).The energy absorption and peak crash force of the simplified vehicle model under the full-lap frontal impact condition are used as the design objectives,with the weighted sum of the basic frequency,the first-order torsional and bending frequencies of the full-size vehicle model,and the weight of the front body taken as the constraints.The thicknesses of nine components on the front body are defined as design variables,and their geometric tolerances determine the uncertainty factor.The most accurate metamodel using the polynomial response surface,kriging,and a radial basis function is selected to model four design criteria during optimization,allowing the efficiency improvement to be computed.Monte Carlo simulations are adopted to handle the probability constraints,and multi-objective particle swarm optimization is employed as the solver.The MORO results indicate reliability levels of R=100%,demonstrating the significant enhancement in reliability of the front body over that given by DMOO,and reliable design schemes and proposals are provided for further study.

Multi-objective Model Predictive Control of Grid-connected Three-level Inverter Based on Hierarchical Optimization

In order to solve the problem of weighting factors selection in the conventional finite-control-set model predictive control for a grid-connected three-level inverter,an improved multi-objective model predictive control without weighting factors based on hierarchical optimization is proposed.Four control objectives are considered in this strategy.The grid current and neutral-point voltage of the DC-link are taken as the objectives in the first optimization hierarchy,and by using fuzzy satisfaction decision,several feasible candidates of voltage vectors are determined.Then,the average switching frequency and common-mode voltage are optimized in the second hierarchy.The average ranking criterion is introduced to sort the objective functions,and the best voltage vector is obtained to realize the coordinated control of multiple objectives.At last,the effectiveness of the proposed strategy is verified by simulation results.

复杂测压管路系统动态特性的通用分析程序

利用流体管道耗散模型 ,建立可用于计算复杂传压管路动态特性的方法和通用计算程序 ,结果用于脉动风压测量的畸变信号修正 .与相关文献比较 ,使用本方法可以灵活地处理更为复杂的管路情况 .定义了衡量管路动态特性品质的控制目标函数 ,并导出了该函数对各种管路参数灵敏度的解析表达式 ,以便于提高管路优化计算的效率 .最后结合两个算例 ,证明了本方法的有效性 .根据计算结果 ,推荐了适合于简单逐点测压试验应用的一组简单优化管路配置方案 ,可供脉动风压测试风洞试验中的传压管路设计时参考 .

多传感器水色卫星数据融合在提高观测数据质量中的作用

多传感器水色卫星融合对于提高水色数据时空覆盖率和可靠性具有重要的意义。利用MODIS、MERIS和VIIRS多传感器水色卫星融合数据与时空匹配的现场实测数据进行对比,分析了两种融合方法(平均法和基于最优化技术的光学融合算法)在提高观测数据质量中的作用。结果表明,与现场实测数据对比,MERIS传感器测量的平均绝对百分比误差(MAPE)为18.5%,MODIS传感器的MAPE为13.4%,VIIRS传感器的MAPE为18.2%,平均法融合产品的MAPE为12.8%,基于最优化技术的光学融合产品的MAPE为9.6%,说明两种融合算法都能降低测量误差,通过数据融合可以显著地提高数据质量,其中基于最优化技术的光学融合法性能更优。

Analytic design of information granulation-based fuzzy radial basis function neural networks with the aid of multiobjective particle swarm optimization

Purpose–The purpose of this paper is to consider the concept of Fuzzy Radial Basis Function Neural Networks with Information Granulation(IG-FRBFNN)and their optimization realized by means of the Multiobjective Particle Swarm Optimization(MOPSO).Design/methodology/approach–In fuzzy modeling,complexity,interpretability(or simplicity)as well as accuracy of the obtained model are essential design criteria.Since the performance of the IG-RBFNN model is directly affected by some parameters,such as the fuzzification coefficient used in the FCM,the number of rules and the orders of the polynomials in the consequent parts of the rules,the authors carry out both structural as well as parametric optimization of the network.A multi-objective Particle Swarm Optimization using Crowding Distance(MOPSO-CD)as well as O/WLS learning-based optimization are exploited to carry out the structural and parametric optimization of the model,respectively,while the optimization is of multiobjective character as it is aimed at the simultaneous minimization of complexity and maximization of accuracy.Findings–The performance of the proposed model is illustrated with the aid of three examples.The proposed optimization method leads to an accurate and highly interpretable fuzzy model.Originality/value–A MOPSO-CD as well as O/WLS learning-based optimization are exploited,respectively,to carry out the structural and parametric optimization of the model.As a result,the proposed methodology is interesting for designing an accurate and highly interpretable fuzzy model.

石油化工行业突发性事故的预测与预防

突发性事故一直是困扰石油化工行业安全经济生产运行的一个难题,多发性的突发事故将造成装置的非计划停车,轻则设备、物料受损,重则导致人员伤亡的发生。采用离散和连续空间的搜索技术原理,寻找和分析事故发生的原因,并对突发事故发生进行预测与预防;提出故障点归零法和木桶原理等预防法,进行单套装置和全系统的整体性防御,以减少或避免石化行业装置的非计划停车,保障设备、物料和人员的安全。

Best compromising crashworthiness design of automotive S-rail using TOPSIS and modified NSGAⅡ

In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.

A discussion of objective function representation methods in global optimization

Non-convex optimization can be found in several smart manufacturing systems. This paper presents a short review on global optimization(GO) methods. We examine decomposition techniques and classify GO problems on the basis of objective function representation and decomposition techniques. We then explain Kolmogorov's superposition and its application in GO. Finally,we conclude the paper by exploring the importance of objective function representation in integrated artificial intelligence, optimization, and decision support systems in smart manufacturing and Industry 4.0.

THE OPTIMAL REPLACEMENT FOR ADDITIVE DAMAGE MODELS IN DISCRETE SETTING

A system receives shocks at successive random points of discrete time, and each shock causes a positive integer-valued random amount of damage which accumulates on the system one after another. The system is subject to failure and it fails once the total cumulative damage level first exceeds a fixed threshold. Upon failure the system must be replaced by a new and identical one and a cost is incurred. If the system is replaced before failure, a lower cost is incurred.On the basis of some assumptions, we specify a replacement rule which minimizes the longrun (expected) average cost per unit time and possesses the control limit property, Finally, an algorithm is discussed in a special case.