Multi-objective integrated optimization based on evolutionary strategy with a dynamic weighting schedule

The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.

改进量子位初始映射的综合SWAP优化策略

在嘈杂的中尺度量子时代,传统的初始映射策略忽略了后续操作中可能出现的邻接性的问题。针对这一挑战,综合考虑量子比特间的距离、交互时间和门操作的错误率,设计了一个多因素交互成本函数,并提出综合SWAP优化策略(comprehensive SWAP optimization strategy,CSOS)。该策略包括最佳SWAP选择和基于SWAP的批量更新策略,用于优化量子电路的局部量子位映射。最佳SWAP选择通过对比SWAP操作的效益,选择最佳收益的SWAP门;批量更新策略在映射阶段考虑即将执行的量子操作序列,预先执行批量的SWAP操作。二者综合可以减少整个电路执行过程中的SWAP数量,以最大程度减少映射开销。实验结果显示,CSOS优化方式可以平均减少38.1%的插入SWAP门数量,并降低约12%的硬件门计数开销。

Simulation-based multi-objective optimization for roll shifting strategy in hot strip mill

A simulation-based multi-objective optimization approach for roll shifting strategy in hot strip mills was presented. Firstly, the effect of roll shifting strategy on wear contour was investigated by mtmerical simulation, and two evaluation indexes including edge smoothness and body smoothness of wear contours were introduced. Secondly, the edge smoothness average and body smoothness average of all the strips in a rolling campaign were selected as objective functions, and shifting control parameters as decision variables, the multi-objective method of MODE/D as the optimizer, and then a simulation-based multi-objective optimization model for roll shifting strategy was built. The experimental result shows that MODE/D can obtain a good Pareto-optimal front, which suggests a series of alternative solutions to roll shifting strategy. Moreover, the conflicting relationship between two objectives can also be found, which indicates another advantage of multi-objective optimization. Finally, industrial test confirms the feasibility of the multi-objective approach for roll shifting strategy, and it can improve strip profile and extend same width rolling miles of a rolling campaign from 35 km to 70 km.

Operation Strategy of EV Battery Charging and Swapping Station

An operation strategy of the electric vehicle （EV） battery charging and swapping station is proposed in the paper. The strategy is established based on comprehensively consideration of the EV charging behaviors and the possible mutual actions between battery charging and swapping. Three energy management strategies can be used in the station： charging period shifting, energy exchange between EVs, and energy supporting from surplus swapping batteries. Then an optimization model which minimizes the total energy management costs of the station is built. The Monte Carlo simulation is applied to analyze the characteristics of the EV battery charging load, and a heuristic algorithm is used to solve the strategy providing the relevant information of EVs and the battery charging and swapping station. The operation strategy can efficiently reduce battery charging during the high electricity price periods and make more reasonable use of the resources. Simulations prove the feasibility and rationality of the strategy.

Multi-objective optimization for roll shifting strategy in cross rolling campaigns of hot strip mill

A multi-objective optimization approach for the roll shifting strategy in cross rolling campaigns of hot strip mills is presented. The effect of different roll shifting strategies on roll wear contour is studied by numerical simulation, and two evaluation indexes ,namely body smoothness and edge smoothness, are proposed. The average body smoothness and average rolling edge smoothness of all strips in a rolling campaign are taken as the objective functions, the shifting positions of all wide strips as the decision variables, and the multi-objective method of NSGA-II as the optimizer. Thus a multi-objective optimization model for the roll shifting strategy is built. The simulation results show that work roll shifting can make wear contour smooth,and a dish-shaped wear contour without severe local wear can be achieved by the roll shifting strategy with varying stroke. Optimization experimentation shows that by means of NSGA-II,a good Pareto-optimal front can be obtained, which suggests a series of alternative solutions for roll shifting strategy optimization. The experimentation also shows that there is a conflict between the two objectives. Finally, application cases confirm the feasibility of the multi-objective approach, which can improve the strip profile ,reduce edge waves and extend the rolling miles of a rolling campaign.

The Research of Examination Paper Generation Based on Index System Metrics and Multi-Objective Strategy

Since the examination paper generated with computer by the algorithms of random and backtracking takes on inferior quality and inefficient, and the question of generating examination paper with computer has the character of multi-ob-jective because of the index system metrics, the genetic algorithm with multi-objective strategy optimization is proposed to solve this problem. Mapping the index system to multi-objective functions and optimizing the computing with multi-objective strategy are employed in the algorithm. The genetic algorithm experiment based on the multi-objective strategy optimization shows that the result has the advantages getting tradeoff between performance and quality, and having the ability to tune the performance and quality to meet the user’s requirements.

Non-dominated sorting culture differential evolution algorithm for multi-objective optimal operation of Wind-Solar-Hydro complementary power generation system

Due to the intermittency and instability of Wind-Solar energy and easy compensation of hydropower, this study proposes a Wind-Solar-Hydro power optimal scheduling model. This model is aimed at maximizing the total system power generation and the minimum ten-day joint output. To effectively optimize the multi-objective model, a new algorithm named non-dominated sorting culture differential evolution algorithm(NSCDE) is proposed. The feasibility of NSCDE was verified through several well-known benchmark problems. It was then applied to the Jinping Wind-Solar-Hydro complementary power generation system. The results demonstrate that NSCDE can provide decision makers a series of optimized scheduling schemes.

Multi-objective Function Optimization for Environmental Control of a Greenhouse Based on a RBF and NSGA-Ⅱ

To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱ could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.

考虑换电需求响应的换电站参与电能量-调频市场竞价策略

电动汽车(EV)大规模发展会加大电网的调峰调频压力。通过换电站市场化运营,可有效激励换电站利用EV换电需求和换电站电池灵活性资源,缓解电网调峰调频压力。为此,提出了换电站参与电能量市场和调频辅助服务市场的协同竞价策略及其两阶段优化模型。第1阶段依据调频市场需求制定换电价格,引导EV调整换电需求,优化冗余电池数量及时序,以便与调频需求趋势一致,进而提升换电站调频服务收益;第2阶段考虑换电站需求响应和市场调峰调频需求,制定换电站竞价策略。所提出竞价策略在优先满足EV换电需求的同时,可降低换电站充电成本、提升调频服务收益,并助力电网调峰调频。算例仿真结果验证了所提策略的有效性。

考虑充换电的模块化需求响应公交路径优化

模块车能通过中途分离与合并来调整车队容量、实现无缝换乘,兼具规模化与“门到门”灵活性优势,但其轻量化的电池设计也在一定程度上限制了车辆续航能力。为了探索模块车在需求响应公交中的应用,并解决车辆中途充电问题,本文建立了模块化需求响应公交路径规划模型,优化车辆路径计划、车队编组策略、车内换乘策略以及换电和机会充电计划。针对模型特征设计了改进的自适应大邻域搜索算法,根据各车辆路径之间需要进行编组和协同交互的特点,定制化设计了车队类修复算子和能源类修复算子等。使用安徽宣城的出行数据进行实验,结果显示:与传统公交相比,模块化需求响应公交系统使乘客总出行用时降低48.81%;与车辆单独运行的方案相比,车队编组方案能够使系统总成本平均降低13.24%;相比仅充电策略,充换电结合策略能在少量增加备用电池固定成本的情况下,使能源成本减少21.09%;此外,企业可以通过调整等待时间惩罚系数来平衡企业经营成本与乘客时间成本,达到动态最优。

New operation strategy and multi-objective optimization of hybrid solar-fuel CCHP system with fuel thermochemical conversion and source-loads matching

Multi-energy hybrid energy systems are a promising option to mitigate fluctuations in the renewable energy supply and are crucial in achieving carbon neutrality.Solar-fuel thermochemical hybrid utilization upgrades solar energy to fuel chemical energy,thereby achieving the efficient utilization of solar energy,reducing CO_(2)emission,and improving operation stability.For hybrid solar-fuel thermochemical CCHP systems,conventional integration optimization methods and operation modes do not account for the instability of solar energy,thermochemical conversion,and solar fuel storage.To improve the utilization efficiency of solar energy and fuel and achieve favorable economic and environmental performance,a new operation strategy and the optimization of a mid-and-low temperature solar-fuel thermochemical hybrid CCHP system are proposed herein.The system operation modes for various supply-demand scenarios of solar energy input and thermal-power outputs are analyzed,and a new operation strategy that accounts for the effect of solar energy is proposed,which is superior to conventional CCHP system strategies that primarily focus on the balance between system outputs and user loads.To alleviate the challenges of source-load fluctuations and supply-demand mismatches,a multi-objective optimization model is established to optimize the system integration configurations,with objective functions of system energy ratio,cost savings ratio,and CO_(2)emission savings ratio,as well as decision variables of power unit capacity,solar collector area,and syngas storage capacity.The optimization design of the system configuration and the operation strategy improve the performance of the hybrid system.The results show that the system annual energy ratio,cost saving ratio,and CO_(2)emission saving ratio are 52.72%,11.61%,and 36.27%,respectively,whereas the monthly CO_(2)emission reduction rate is 27.3%–47.6%compared with those of reference systems.These promising results will provide useful guidance for the integrated design and operational regulation of hybrid solar-fuel thermochemical systems.

Multi-Objective Sizing of Solar-Wind-Hydro Hybrid Power System with Doubled Energy Storages Under Optimal Coordinated Operational Strategy

More and more attention has been paid to the high penetration of renewable energy in recent years.The randomness and intermittency of solar and wind energy make it an inevitable trend that renewables are coupled with energy storage technologies.Pumped hydro storage(PHS)is the most widelyused storage form in the power grid but the capacity is limited by geographic conditions.The concentrated solar power(CSP)plant with a thermal energy storage(TES)system can realize easier grid connections and effective peak shaving.Therefore,this paper proposes a solar-wind-hydro hybrid power system with PHS-TES double energy storages,and investigates the optimal coordinated operational strategy and multi-objective sizing.The optimal sizing problem which considers the minimum levelized cost of energy(LCOE)and loss of power supply probability(LPSP)as objectives is solved by multi-objective particle swarm optimization.Moreover,the seasonal uncertainties of renewables are considered by applying a scenario-based analysis using Kmeans clustering.Finally,a case study reveals the effectiveness of the coordinated operational strategy and double energy storages from the perspectives of economy and reliability.The comparisons of optimal sizing results show that the PV-WindCSP-PHS system decreases the LCOE by 19.1%compared to a PV-Wind-CSP system under the same LPSP,and reduces the LPSP compared to PV-Wind-PHS systems with limited reservoir capacity,which indicates that the proposed system with double energy storages has better economy and reliability performance compared to single storage.

Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy

To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.

基于换电服务定价策略及动态调控方法的含充换电站微电网系统双层优化调度

换电服务价格高是电动汽车换电模式普及率低的重要因素之一,为了提高换电模式使用程度,充分发挥换电模式参与系统调度时所发挥的削峰填谷作用,该文提出一种考虑用户参与度的换电服务定价策略及动态调控方法。首先,建立计及时间成本的充电服务与换电服务总费用差价模型,并依据消费者心理学原理构建服务差价-用户参与度曲线;其次,制定换电服务定价策略,并提出相应的动态调控方法;最后,建立含充换电站(battery charging and swapping station,BCSS)的微电网联合系统双层优化模型。上层根据换电服务定价策略及动态调控方法,制定出用户参与度高的换电服务电价;下层根据用户响应换电服务电价后的负荷量,以微电网联合系统总运行成本最低为目标调度机组出力,并以用户满意度作为衡量换电服务电价的指标,合理调整下一时段换电服务电价。通过算例分析,所提方法在实现系统负荷削峰的同时,降低微电网联合系统总运行成本,体现了所提定价策略及动态调控方法的有效性。

考虑充放电策略的换电站与风电-碳捕集虚拟电厂的低碳经济调度

风电-碳捕集虚拟电厂与换电站均具有一定程度的低碳性能,有助于电力系统低碳化。考虑系统源荷两侧低碳配合问题,提出一种考虑充放电策略的换电站与风电-碳捕集虚拟电厂协调低碳调度方法。在源侧引入了采用综合灵活运行方式的碳捕集电厂与风电场聚合,形成风电-碳捕集虚拟电厂,在荷侧考虑了具备“源荷”双重角色的换电站,并制定充放电策略,分析二者各自在低碳运行方面的不足之处,研究二者的低碳互补问题,并建立了以系统综合成本最优为目标函数的低碳经济调度模型。最后,采用改进的IEEE 30节点系统进行仿真验证,算例结果表明所提调度模型能在提高全网风电消纳能力的同时,实现系统低碳经济运行。

IBMSMA: An Indicator-based Multi-swarm Slime Mould Algorithm for Multi-objective Truss Optimization Problems

This work proposes an improved multi-objective slime mould algorithm, called IBMSMA, for solving the multi-objective truss optimization problem. In IBMSMA, the chaotic grouping mechanism and dynamic regrouping strategy are employed to improve population diversity;the shift density estimation is used to assess the superiority of search agents and to provide selection pressure for population evolution;and the Pareto external archive is utilized to maintain the convergence and distribution of the non-dominated solution set. To evaluate the performance of IBMSMA, it is applied to eight multi-objective truss optimization problems. The results obtained by IBMSMA are compared with other 14 well-known optimization algorithms on hypervolume, inverted generational distance and spacing-to-extent indicators. The Wilcoxon statistical test and Friedman ranking are used for statistical analysis. The results of this study reveal that IBMSMA can find the Pareto front with better convergence and diversity in less time than state-of-the-art algorithms, demonstrating its capability in tackling large-scale engineering design problems.

多目标轮换策略引导的信息熵阈值分割算法

为了提高信息熵阈值分割算法图像分割的准确性,提出了一种多目标轮换策略引导的信息熵阈值分割算法。综合利用图像的Kapur熵、交叉熵、Renyi熵和Masi熵作为目标函数进行轮换,采用混沌粒子群优化算法寻优,得到一个由4个目标函数生成的解集,利用改进的Xie-Beni指数来测度、选择解集中的最优解,最终利用最优解实现图像分割。实验结果表明,多目标轮换算法的分割准确率能够达到66%~99%。与相关算法相比,所提算法对图像分割的准确性较高。

Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV users.This paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power grid.The proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,simultaneously.Afterwards,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling problem.Finally,simulation studies verify the effectiveness of the proposed multi-objective operation method.

Performance-Driven Multi-Objective Optimization Method for DLR Transonic Tandem Cascade Shape Design

Transonic tandem cascades can effectively increase the working load,and this feature conforms with the requirement of the large loads and pressure ratios of modern axial compressors.This paper presents an optimization strategy for a German Aerospace Center(DLR)transonic tandem cascade,with one front blade and two rear blades,at the inlet Mach number of 1.051.The tandem cascade profile was parameterized using 19 control parameters.Non-dominated sorting Genetic algorithm(NSGA-II)was used to drive the optimization evolution,with the computational fluid dynamics(CFD)-based cascade performances correction added for each generation.Inside the automatic optimization system,a pressure boundary condition iterative algorithm was developed for simulating the cascade performance with a constant supersonic inlet Mach number.The optimization results of the cascade showed that the deflection of the subsonic blade changed evidently.The shock wave intensity of the first blade row was weakened because of the reduced curvatures of the optimized pressure and suction sides of the front blade part and the downstream moved maximum thickness position.The total pressure losses decreased by 15.6%,20.9%and 19.9%with a corresponding increase in cascade static pressure ratio by 1.3%,1.8%and 1.7%,for the three cascade shapes in the Pareto solution sets under the near choke,the design and near stall conditions,respectively.

Efficient multi-objective CMA-ES algorithm assisted by knowledge-extraction-based variable-fidelity surrogate model

To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy(KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions(LF-NDS). Secondly, aiming to obtain the High-Fidelity(HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally,the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement(MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts,the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms.