计及蓄电池寿命的冷热电联供型微电网多目标经济优化运行

冷热电联供（combined cooling,heating and power,CCHP）型微电网不仅能为清洁能源的开发利用提供良好的平台,降低能源消费带来的环境污染,而且能够改善供电电能质量,降低系统损耗。为使CCHP型微电网经济计算更加符合实际运行工况,考虑将蓄电池寿命损耗带来的经济损失加入经济调度计算模型,同时考虑CCHP型微电网的经济性和环保性,建立CCHP型微电网的多目标优化模型。根据最大满意度的原则将多目标优化模型转化为单目标优化模型,利用改进型遗传算法,优化日内各微源的出力。通过算例对比分析多目标优化和各个单目标优化对微电网中各微源出力的影响,结果表明：多目标优化模型能够兼顾CCHP型微电网的经济性和环保性,更加接近CCHP型微电网的实际运行工况。     

Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)—the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.     

Redundancy allocation for multi-state systems using physical programming and genetic algorithms

This paper proposes a multi-objective optimization model for redundancy allocation for multi-state series–parallel systems. This model seeks to maximize system performance utility while minimizing system cost and system weight simultaneously. We use physical programming as an effective approach to optimize the system structure within this multi-objective optimization framework. The physical programming approach offers a flexible and effective way to address the conflicting nature of these different objectives. Genetic algorithm (GA) is used to solve the proposed physical programming-based optimization model due to the following three reasons: (1) the design variables, the number of components of each subsystems, are integer variables; (2) the objective functions in the physical programming-based optimization model do not have nice mathematical properties, and thus traditional optimization approaches are not suitable in this case; (3) GA has good global optimization performance. An example is used to illustrate the flexibility and effectiveness of the proposed physical programming approach over the single-objective method and the fuzzy optimization method.     

Simulation optimization of logistics systems through the use of variance reduction techniques and criterion models

Simulation is a useful tool for modelling logistics systems. However, simulation itself is not an optimization tool. Therefore attempts have been made to combine simulation and optimization. Optimization of a logistics system through the use of simulation is difficult for several reasons. Because of the size and complexity of logistics systems, it is often necessary to consider the trade-offs between multiple conflicting performance measures for the system. One major drawback associated with commercially available tools is that users are not able to consider these trade-offs easily. To overcome this drawback, a simulation model can be developed to employ multi-objective decision analysis techniques such as criterion models which can then be optimized. This article illustrates how criterion models can be interfaced with simulation models of logistics systems. In addition, this article includes the programming and implementation of the variance reduction techniques of common random numbers and antithetic variates.     

Ice thermal energy storage (ITES) for air-conditioning application in full and partial load operating modes

One method for reducing electricity consumption in an air-conditioning (AC) system is using ice thermal energy storage (ITES) system. ITES systems are divided into two categories, full and partial operating modes (FOM and POM). In this study, an AC with ITES system is first modeled and analyzed in energy, exergy, economic and environmental (4E) aspects in two full and partial load operating modes. Multi-objective optimization technique and Genetic Algorithm were used for computing the optimum values of design parameters. Exergy efficiency and total annual cost were considered as two objective functions in multi-objective optimization. The comparison of ITES system in full and partial operating modes with that for traditional system showed reduction in electricity consumption (11.83% for FOM and 10.23% for POM) due to the fact that ITES system produced just as much as cooling load was required. Additionally switching electricity consumption from on-peak to off-peak hours caused a reduction in electricity consumption cost (32.65% for FOM and 13.45% for POM).     

基于整数编码遗传算法的传感器优化配置研究

动态测试中,为了将传感器配置在合理的自由度上,以便充分反映结构的动力特性,需对传感器进行优化配置。本文分别以模态置信度矩阵、Fisher信息阵和它们的组合为优化准则,采用整数编码遗传算法,探讨了单目标和多目标优化的传感器优化配置问题。通过与现有的模态动能法、有效独立法及基于QR分解的逐步累积法进行比较,传感器优化配置的结果表明整数编码遗传算法优于上述三种现有方法。     

基于NSGA-Ⅱ算法的变温热源内可逆简单MCBC的性能优化

基于前人所建立的变温热源内可逆简单等温加热修正的闭式布雷顿循环（modified closed Brayton cycle,MCBC）模型,分析了压气机压比等参数对循环性能的影响。分别以无因次功率和无因次生态学函数为优化目标,以压比和各个换热器的热导率分配为优化变量,对变温热源内可逆简单MCBC进行单目标优化。最后基于NSGA-Ⅱ算法,以无因次功率、热效率和无因次生态学函数为优化目标,以压比和各个换热器的热导率分配为优化变量,对变温热源内可逆简单MCBC进行多目标优化,并分析了相关参数的灵敏度。结果表明：常规燃烧室（regular combustion chamber,RCC）和收敛型燃烧室（converging combustion chamber,CCC）外侧流体的入口温比对单目标优化结果的影响存在着明显的相互关系;与单目标优化得到的结果相比,多目标优化得到的最优解对应的偏差指数更小,其中通过香农熵决策得到的偏差指数最小;优化变量变化±10%对最优无因次功率、最优热效率、最优无因次生态学函数及其对应的等温压降比的影响很小,其变化范围均不超过5%。     

Path synthesis of four-bar mechanisms using synergy of polynomial neural network and Stackelberg game theory

In this article, a novel approach based on game theory is presented for multi-objective optimal synthesis of four-bar mechanisms. The multi-objective optimization problem is modelled as a Stackelberg game. The more important objective function, tracking error, is considered as the leader, and the other objective function, deviation of the transmission angle from 90° (TA), is considered as the follower. In a new approach, a group method of data handling (GMDH)-type neural network is also utilized to construct an approximate model for the rational reaction set (RRS) of the follower. Using the proposed game-theoretic approach, the multi-objective optimal synthesis of a four-bar mechanism is then cast into a single-objective optimal synthesis using the leader variables and the obtained RRS of the follower. The superiority of using the synergy game-theoretic method of Stackelberg with a GMDH-type neural network is demonstrated for two case studies on the synthesis of four-bar mechanisms.     

Thermodynamic optimization of reverse Brayton cycles of different configurations for cryogenic applications

The thermodynamic optimization of differing Reverse Brayton Refrigeration (RBR) cycle configurations is presented in this study. These cycle configurations include: Conventional 1-stage compression cycle; Conventional 2-stage compression cycle; 1-stage compression Modified cycle with intermediate cooling of the recuperator using an auxiliary cooler; and an Integrated 2-stage expansion RBR cycle. For high pressure ratio applications, multi-stage compressors with intercooling are considered. Analytical solutions for the conventional cycles are developed including thermal and fluid flow irreversibilities of the recuperators and all heat exchangers in addition to the compression and expansion processes. Exergy analysis is performed and the exergy destruction of different components of the RBR cycles for different configurations is presented and the effects of important system parameters on performance are investigated. Thermodynamic optimization of the cycles with intermediate cooling of the recuperator is included. Effects of the 2nd law/exergy efficiency of the auxiliary cooler on the total system efficiencies are presented.     

Identification of vehicle suspension parameters by design optimization

The design of a vehicle suspension system through simulation requires accurate representation of the design parameters. These parameters are usually difficult to measure or sometimes unavailable. This article proposes an efficient approach to identify the unknown parameters through optimization based on experimental results, where the covariance matrix adaptation–evolutionary strategy (CMA-es) is utilized to improve the simulation and experimental results against the kinematic and compliance tests. This speeds up the design and development cycle by recovering all the unknown data with respect to a set of kinematic measurements through a single optimization process. A case study employing a McPherson strut suspension system is modelled in a multi-body dynamic system. Three kinematic and compliance tests are examined, namely, vertical parallel wheel travel, opposite wheel travel and single wheel travel. The problem is formulated as a multi-objective optimization problem with 40 objectives and 49 design parameters. A hierarchical clustering method based on global sensitivity analysis is used to reduce the number of objectives to 30 by grouping correlated objectives together. Then, a dynamic summation of rank value is used as pseudo-objective functions to reformulate the multi-objective optimization to a single-objective optimization problem. The optimized results show a significant improvement in the correlation between the simulated model and the experimental model. Once accurate representation of the vehicle suspension model is achieved, further analysis, such as ride and handling performances, can be implemented for further optimization.     

Multi-objective optimization of space station logistics strategies using physical programming

This study extends a previously proposed single-objective optimization formulation of space station logistics strategies to multi-objective optimization. The four-objective model seeks to maximize the mean utilization capacity index, total utilization capacity index, logistics robustness index and flight independency index, aiming to improve both the utilization benefit and the operational robustness of a space station operational scenario. Physical programming is employed to convert the four-objective optimization problem into a single-objective problem. A genetic algorithm is proposed to solve the resulting physical programming-based optimization problem. Moreover, the non-dominated sorting genetic algorithm-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the physical programming-based solution. The proposed approach is demonstrated with a notional one-year scenario of China's future space station. It is shown that the designer-preferred compromise solution improving both the utilization benefit and the operational robustness is successfully obtained.     

Alternatives and challenges in optimizing industrial safety using genetic algorithms

Safety (S) improvement of industrial installations leans on the optimal allocation of designs that use more reliable equipment and testing and maintenance activities to assure a high level of reliability, availability and maintainability (RAM) for their safety-related systems. However, this also requires assigning a certain amount of resources (C) that are usually limited. Therefore, the decision-maker in this context faces in general a multiple-objective optimization problem (MOP) based on RAMS+C criteria where the parameters of design, testing and maintenance act as decision variables. Solutions to the MOP can be obtained by solving the problem directly, or by transforming it into several single-objective problems. A general framework for such MOP based on RAMS+C criteria is proposed in this paper. Then, problem formulation and fundamentals of two major groups of resolution alternatives are presented. Next, both alternatives are implemented in this paper using genetic algorithms (GAs), named single-objective GA and multi-objective GA, respectively, which are then used in the case of application to solve the problem of testing and maintenance optimization based on unavailability and cost criteria. The results show the capabilities and limitations of both approaches. Based on them, future challenges are identified in this field and guidelines provided for further research.     

Multi-objective Optimization of Co-cured Composite Laminates with Embedded Viscoelastic Damping Layer

Presented herein is a methodology for the multi-objective optimization of damping and bending sti-ness of co-coured composite laminates with embedded viscoelastic damping layer.The embedded viscoelastic damping layer is perforated with a series of small holes,and the ratio of the perforation area to the total damping area is the design variable of the methodology.The multi-objective optimization is converted into a single-objective problem by an evaluation function which is a liner weigh sum of the two sub-...     

基于分析的空调系统运行控制的多目标优化

针对空调系统运行效率低下,且运行过程中子系统之间相互影响、相互制约的问题,本文基于分析方法提出一种空调系统多目标运行优化方法,以提高空调系统整体的运行效率。以某机场航站楼空调系统为研究对象,采用分析方法建立了空调系统的生产结构图,并根据生产结构将空调系统划分为3个子系统。在建立子系统分析模型的基础上,以3个子系统的最小损为优化目标,采用多目标粒子群优化算法(MOPSO)对空调系统的运行参数进行优化。针对某一典型日的运行工况对空调系统进行多目标优化,结果表明:相对于空调系统的原始控制方式,采用多目标运行优化方法可以有效减小空调各个子系统以及整个系统的损,使空调系统的运行效率明显提高,达到了节能的目的。     

星载天线反射面传感器多目标优化部署方案

传感器数量和位置的优化部署,是实现大型星载天线在轨获取高精度模态参数亟待解决的关键技术。为克服以往研究中采用单一优化准则所带来的局限性和片面性,设计观测信息正交性最大和能量最大的双优化准则,引入NSGA-II算法进行多目标传感器优化部署求解。考虑到该算法仅适合连续性优化变量,存在收敛速度及多样性保持方面的不足,对其在编码方式和遗传算子设计两方面进行改进,并给出所有指标权重组合且分布均匀的Pareto最优解集。设计四种优化方案,进行仿真比较可得：基于改进NSGA-II算法的星载天线传感器多目标优化部署方案,较其他三种方案在性能指标上最优,且该方案更加符合实际工程的多指标优化设计要求,保证优化结果具有更高的灵活性和适应性。     

Explicit consideration of multiple objectives in cellular manufacturing

Although many methodologies have been proposed for solving the cell-formation problem, few of them explicitly consider the existence of multiple objectives in the design process. In this article, the development of multi-objective genetic programming single-linkage cluster analysis (GP-SLCA), an evolutionary methodology for the solution of the multi-objective cell-formation problem, is described. The proposed methodology combines an existing algorithm for the solution of single-objective cell-formation problems with NSGA-II, an elitist evolutionary multi-objective optimization technique. Multi-objective GP-SLCA is able to generate automatically a set of non-dominated solutions for a given multi-objective cell-formation problem. The benefits of the proposed approach are illustrated using an example test problem taken from the literature and an industrial case study.     

结构主动控制的一体化多目标优化研究

基于Pareto多目标遗传算法提出了结构主动控制系统的一体化多目标优化设计方法,对作动器位置与主动控制器进行同步优化设计.外界激励采用平稳过滤白噪声来模拟,在状态空间下通过求解Lyapunov方程,得到结构响应和主动控制力的均方值.主动控制器采用LQG控制算法来进行设计.以结构位移和加速度均方值最大值与相应无控响应均方值的最大值之比,以及所需控制力均方值之和作为多目标同步优化的目标函数.优化过程还考虑了结构与激励参数对优化结果的影响.最后以某6层平面框架有限元模型为例进行了计算机仿真分析,结果表明所提出的主动控制系统多目标一体化优化方法简单,高效,实用,具有较好的普适性.     

Two efficient algorithms for constructing almost even approximations of the Pareto front in multi-objective optimization problems

In this article, two algorithms are proposed for constructing almost even approximations of the Pareto front of multi-objective optimization problems. The first algorithm is a hybrid of the ε-constraint and Pascoletti–Serafini scalarization methods for solving bi-objective problems. The second is a modification of the successive Pareto optimization (SPO) algorithm for solving three-objective problems. In these algorithms, the MATLAB fmincon solver is used to solve single-objective optimization problems, which returns a local optimal solution. Some metrics are considered to evaluate the quality of approximations obtained by the suggested algorithms on six test problems, and their results are compared with other algorithms (normal constraint, weighted constraint, SPO, differential evolution, multi-objective evolutionary algorithm/decomposition–differential evolution, non-dominated sorting genetic algorithm-II and S-metric selection evolutionary multi-objective algorithm). Experimental results show that the proposed algorithms provide almost even approximations of the whole Pareto front, and better quality of approximation and CPU time compared with established algorithms.     

Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations

In addition to energy consumption, the use of cutting fluids, deposition of worn tools and certain other manufacturing activities can have environmental impacts. All these activities cause carbon emission directly or indirectly; therefore, carbon emission can be used as an environmental criterion for machining systems. In this article, a direct method is proposed to quantify the carbon emissions in turning operations. To determine the coefficients in the quantitative method, real experimental data were obtained and analysed in MATLAB. Moreover, a multi-objective teaching–learning-based optimization algorithm is proposed, and two objectives to minimize carbon emissions and operation time are considered simultaneously. Cutting parameters were optimized by the proposed algorithm. Finally, the analytic hierarchy process was used to determine the optimal solution, which was found to be more environmentally friendly than the cutting parameters determined by the design of experiments method.