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1.
设计了一种基于支配关系下的局部搜索方法，将此局部搜索方法嵌入到多目标遗传算法中，从而提出一种有效的求解多目标优化问题的混合遗传算法。为加速遗传算法在全局优化问题上的收敛性，发挥传统数值优化算法在计算速度与计算精度上的优势，在遗传算法中镶嵌一个多目标线搜索算子。线搜索算子与遗传算法中的选择算子、交叉算子和变异算子共同作用，使全局搜索和局部搜索都能够很好的实现。数值实验表明，该混合遗传算法能求得问题的数量更多、分布更广的Pareto最优解。 相似文献
2.
A multi-objective optimal operation model of water-sedimentation-power in reservoir is established with power-generation,
sedimentation and water storage taken into account. Moreover, the inertia weight self-adjusting mechanism and Pareto-optimal
archive are introduced into the particle swarm optimization and an improved multi-objective particle swarm optimization (IMOPSO)
is proposed. The IMOPSO is employed to solve the optimal model and obtain the Pareto-optimal front. The multi-objective optimal
operation of Wanjiazhai Reservoir during the spring breakup was investigated with three typical flood hydrographs. The results
show that the former method is able to obtain the Pareto-optimal front with a uniform distribution property. Different regions
(A, B, C) of the Pareto-optimal front correspond to the optimized schemes in terms of the objectives of sediment deposition,
sediment deposition and power generation, and power generation, respectively. The level hydrographs and outflow hydrographs
show the operation of the reservoir in details. Compared with the non-dominated sorting genetic algorithm-II (NSGA-II), IMOPSO
has close global optimization capability and is suitable for multi-objective optimization problems.
LI Hui, born in 1981, male, doctorate student.
Supported by National Science Fund for Distinguished Young Scholars (No.50725929) and National Natural Science Foundation
of China (No.50539060,50679052). 相似文献
3.
Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms 总被引：1，自引：0，他引：1
José D.MARTNEZ-MORALES Elvia R.PALACIOS-HERNNDEZ Gerardo A.VELZQUEZ-CARRILLO 《浙江大学学报(A卷英文版)》2013,14(9):657-670
In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively. 相似文献
4.
With the continuous improvement of the train speed, the dynamic environment of trains turns out to be aerodynamic domination. Solving the aerodynamic problems has become one of the key factors of the high-speed train head design. Given that the aerodynamic drag is a significant factor that restrains train speed and energy conservation, reducing the aerodynamic drag is thus an important consideration of the high-speed train head design. However, the reduction of the aerodynamic drag may increase other aerodynamic forces （moments）, possibly deteriorating the operational safety of the train. The multi-objective optimization design method of the high-speed train head was proposed in this paper, and the aerodynamic drag and load reduction factor were set to be optimization objectives. The automatic multi-objective optimization design of the high-speed train head can be achieved by integrating a series of procedures into the multi-objective optimization algorithm, such as the establishment of 3D parametric model, the aerodynamic mesh generation, the calculation of the flow field around the train, and the vehicle system dynamics. The correlation between the optimization objectives and optimization variables was analyzed to obtain the most important optimization variables, and a further analysis of the nonlinear relationship between the key optimization variables and the optimization objec- tives was obtained. After optimization, the aerodynamic drag of optimized train was reduced by up to 4.15%, and the load re- duction factor was reduced by up to 1.72%. 相似文献
5.
This paper presents a novel approach to find optimum locations and capacity of flexible alternating current transmission system （FACTS） devices in a power system using a multi-objective optimization function. Thyristor controlled series compensators （TCSCs） and static var compensators （SVCs） are the utilized FACTS devices. Our objectives are active power loss reduction, newly introduced FACTS devices cost reduction, voltage deviation reduction, and increase on the robustness of the security margin against voltage collapse. The operational and controlling constraints, as well as load constraints, were considered in the optimum allocation. A goal attainment method based on the genetic algorithm （GA） was used to approach the global optimum. The estimated annual load profile was utilized in a sequential quadratic programming （SQP） optimization sub-problem to the optimum siting and sizing of FACTS devices. Fars Regional Electric Network was selected as a practical system to validate the performance and effectiveness of the proposed method. The entire investment of the FACTS devices was paid offand an additional 2.4% savings was made. The cost reduction of peak point power generation implies that power plant expansion can be postponed. 相似文献
6.
The problem of adaptive multi-objective optimization(AMOO) has received extensive attention due to its practical significance.An important issue in optimizing a multi-objective system is adjusting the weighting coefficients of multiple objectives so as to keep track of various conditions.In this paper,a feedback structure for AMOO is designed.Moreover,the reinforcement learning combined with hidden biasing information is applied to online tuning weighting coefficients of objective functions.Finally,the prop... 相似文献
7.
针对多目标优化问题,提出了一种变加权的多目标混沌优化方法,通过对多目标的随机加权处理,实现了算法在各个方向的搜索,能够找到不同方向的Pareto最优解。与混沌优化方法的结合使该方法不仅能够找到分布比较均匀的Pareto边界上的最优解,而且使用简单、方便。 相似文献
8.
基于Pareto遗传算法的多目标优化 总被引：3，自引：1，他引：2
张林家 《鞍山师范学院学报》2008,10(4)
在工程实际当中存在着大量的多目标优化问题,传统的多目标优化方法存在着明显的缺陷.本文介绍一种基于Pareto最优概念的遗传算法来求解多目标优化问题.这种方法能够给出多目标优化问题的Pareto解集,而不是单纯的一个解,从而可以帮助决策者在Pareto解集中挑选适合设计要求的解作为最终解. 相似文献
9.
太阳能电动汽车的复合能源系统优化匹配问题可以看成一个多目标优化问题,两个相互冲突的目标是极大化系统的峰值功率满足率和极小化系统的成本,前者关系到系统的可靠性后者涉及到样车能否量产,所以两个优化目标都很重要.本文提出了改进的粒子群算法优化配置太阳能电动汽车复合能源系统,这种改进的粒子群算法引进了遗传算法里的变异算子,并且打破常规算法里的加速因子为常数的惯例而使加速因子随时间改变.优化结果显示：改进的粒子群算法也能够很好地解决复合能源系统的多目标优化问题. 相似文献
10.
SUDHAKARAN M. AJAY-D-VIMALRAJ P. PALANIVELU T.G. 《浙江大学学报(A卷英文版)》2007,8(6):896-903
This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear characteristics of the generators, such as prohibited operating zones, ramp rate limits and non-smooth cost functions of the practical generator operation are considered. The proposed hybrid algorithm is demonstrated for three different systems and the performance is compared with the GA and PSO in terms of solution quality and computation efficiency. Comparison of results proved that the proposed algo- rithm can obtain higher quality solutions efficiently in ED problems. A comprehensive software package is developed using MATLAB. 相似文献
11.
提出一种用于电力系统经济负荷分配的改进混沌粒子群算法．算法中采用自适应外罚函数法解决目标函数的约束问题,考虑了机组的系统平衡、出力上下限、爬坡速率和工作死区等约束条件;在粒子群算法中引入混沌机制,使算法能快速跳出局部极值区,提高算法的全局寻优性能;针对变惯性权重系数和变最大搜索速度改进措施的不足,提出依据机组爬坡速率约束来缩小最优解的搜索区域．仿真结果表明,改进的混沌粒子群算法对于解决带约束条件的经济负荷分配问题是可行和高效的,与改进前的计算方法相比,降低了运行费用,提高了寻优速度． 相似文献
12.
13.
INTRODUCTION In the past two decades, a large number ostrategies for control systems analysis and synthesis such as H2, H∞, l1 and μ-synthesis had beendeveloped. In H∞design, all disturbances arelumped into a single norm rather than boundedseparately by the size of each disturbance as ||d||2=||d1||2 … ||dm||2. This certainly leads to some conservatism (D’Andrea, 1999). In contrast, theμ-synthesis technique overcomes the conservatismby introducing structured uncertainty block… 相似文献
14.
This paper proposes a new approach for multi-objective robust control.The approach extends the standard generalized l2(Gl2)and generalized H2(GH2)conditions to a set of new linear matrix inequality(LMI)constraints based on a new stability condition.A technique for variable parameterization is introduced to the multi-objective control problem to preserve the linearity of the synthesis variables.Consequently,the multi-channel multi-objective mixed Gl2/GH2 control problem can be solved less conservatively using computationally tractable algorithms developed in the paper. 相似文献
15.
Jun-hai SHI Zhi-dan ZHONG Xin-jian ZHU Guang-yi CAO 《浙江大学学报(A卷英文版)》2008,9(3):401-409
This study presents a robust design method for autonomous photovoltaic （PV）-wind hybrid power systems to obtain an optimum system configuration insensitive to design variable variations. This issue has been formulated as a constraint multi-objective optimization problem, which is solved by a multi-objective genetic algorithm, NSGA-II. Monte Carlo Simulation （MCS） method, combined with Latin Hypercube Sampling （LHS）, is applied to evaluate the stochastic system performance. The potential of the proposed method has been demonstrated by a conceptual system design. A comparative study between the proposed robust method and the deterministic method presented in literature has been conducted, The results indicate that the proposed method can find a large mount of Pareto optimal system configurations with better compromising performance than the deterministic method. The trade-off information may be derived by a systematical comparison of these configurations, The proposed robust design method should be useful for hybrid power systems that require both optimality and robustness. 相似文献
16.
粒子群优化算法是一种基于群智能的优化方法,规则简单,收敛速度快.将此算法应用于重载齿轮的多目标优化设计,建立基于粒子群优化算法的重载齿轮多目标优化设计的数学模型,实践表明可以快速、有效地求得齿轮优化解. 相似文献
17.
To improve the aerodynamic performance of high-speed trains (HSTs) running in the open air, a multi-objective aerodynamic optimization design method for the head shape of a HST is proposed in this paper. A parametric model of the HST was established and seven design variables of the head shape were extracted. Sample points and their exact values of optimization objectives were obtained by an optimal Latin hypercube sampling (opt. LHS) plan and computational fluid dynamic (CFD) simulations, respectively. A Kriging surrogate model was constructed based on the sample points and their optimization objectives. Taking the total aerodynamic drag force and the aerodynamic lift force of the tail coach as the optimization objectives, the multi-objective aerodynamic optimization design was performed based on a non-dominated sorting genetic algorithm-II (NSGA-II) and the Kriging model. After optimization, a series of Pareto-optimal head shapes were obtained. An optimal head shape was selected from the Pareto-optimal head shapes, and the aerodynamic performance of the HST with the optimal head shape was compared with that of the original train in conditions with and without crosswinds. Compared with the original train, the total aerodynamic drag force is reduced by 2.61% and the lift force of the tail coach is reduced by 9.90% in conditions without crosswind. Moreover, the optimal train benefits from lower fluctuations in aerodynamic loads in crosswind conditions. 相似文献
18.
一种离散型多目标粒子群优化算法 总被引：1，自引：1，他引：0
为获得更好的非劣前端,提出一种离散型多目标粒子群优化算法。该算法根据离散型多目标优化问题的特点,将种群分成多个子种群,在各个子种群中利用表现型共享的适应度函数选择每个子种群的最优粒子。通过多个最优粒子的引导,使整个种群分布更均匀,避免陷入局部最优,保证了解的多样性。实验表明了该算法的有效性。 相似文献
19.
一类农业生产问题的多目标规划模型及解法 总被引：3，自引：0，他引：3
多目标最优化作为运筹学的一个重要分支，在农业的投入产出方面有重要的应用．本文以养殖业为例建立了一类农业生产问题的多目标规划模型，并运用多目标乘除法、功效系数法、评价函数法求解了该模型． 相似文献
20.
针对一个Pareto局部搜索(PLS)算法在解决多目标组合优化问题中所得到的解集与初始点的选取有关,提出该算法的改进。改进算法从初始解开始进行PLS搜索产生一组改进解集VF,然后对VF中的所有解再进行PLS搜索,如此重复直到满足终止条件。实例计算表明,PLSⅠ算法和算法Ⅱ能得到很好的解且解的质量优于PLS算法。 相似文献