会议筹备问题的多目标最优化模型

利用2009年全国大学生数学建模竞赛D题的会议筹备问题,通过预测与会代表总人数和合理的住宿安排方案,建立预订宾馆客房的多目标最优化模型;在租借会议室和租用客车上采用等可能假设,并给出费用的最优化模型。最后利用LING09．0得出会议筹备总费用的全局最优解。     

Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms

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.     

Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-Ⅱ

Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Aigorithm-Ⅱ (NSGA-Ⅱ) can be used to solve the altered multi-objective optimization model. The introduction of NSGA-Ⅱ into water supply network optimal rehabilitation problem solves the conflict between one fitness value of standard genetic algorithm (SGA) and multi-objectives of rehabilitation problem. And the uncertainties brought by using weight coefficients or punish functions in conventional methods are controlled. And also by introduction of artificial inducement mutation (AIM) operation, the convergence speed of population is accelerated; this operation not only improves the convergence speed, but also improves the rationality and feasibility of solutions.     

基于遗传算法的电梯群控研究

针对电梯群控系统的特点,设计出一种更适合的梯群控制的遗传算法.本算法中采用了整数编码和可进行种群竞争的双种群机制,设计了以候梯时间、乘梯时间、系统能耗为群控目标的多目标适应度函数.并在选择操作中引入个体最优选择策略,在交叉操作中构造了与遗传代的数目、预交叉个体本身特点相结合的交叉方式,在变异操作中应用了两点对换和位点变异相结合的变异方法,并设计了从最优解集合中选择最优解的评价函数.经过模拟仿真,运行实验结果表明了此方案的可行性和优越性.     

贫困山区县级土地利用结构调整模型研究

本文从农业生态经济系统和土地资源二个层次的分析入手,揭示了贫困山区县农业生态经济系统功能退化和土地资源利用不合理等普遍性问题。确立了以重建良性农业生态经济系统为总目标的土地利用结构调整基本方向,对用地矛盾众多的贫困山区县,运用多目标规划方法建立了土地利用结构调整模型,为贫困山区县级土地利用结构调整提供了一种可借鉴的思路和方法。     

基于ε支配的MOGA在0／1背包问题中的应用

多目标遗传算法NSGA—Ⅱ是解决0／1背包问题^[1]的有效算法,但是它还存在一定的缺陷,当0／1背包问题的规模较大时,这种方法很难收敛到Pareto最优边界,因此解的分布性不是很好,解集也很难收敛。针对此问题,提出基于ε支配的MOGA来求解0／1背包问题,通过实验验证该算法在求解分布性上优于NSGA-Ⅱ。     

Multi-objective meta level soft computing-based evolutionary structural design

Evolutionary structural design has been the topic of much recent research; however, such designs are usually hampered by the time-consuming stage of prototype evaluations using standard finite element analysis (FEA). Replacing the time-consuming FEA by neural network approximations may be a computationally efficient alternative, but the error in such approximation may misguide the optimization procedure. In this paper, a multi-objective meta-level (MOML) soft computing-based evolutionary scheme is proposed that aims to strike a balance between accuracy vs. computational efficiency and exploration vs. exploitation. The neural network (NN) is used here as a pre-filter when fitness is estimated to be of lesser significance while the standard FEA is used for solutions that may be optimal in their current population. Furthermore, a fuzzy controller updates parameters of the genetic algorithm (GA) in order to balance exploitation vs. exploration in the search process, and the multi-objective GA optimizes parameters of the membership functions in the fuzzy controller. The algorithm is first optimized on two benchmark problems, i.e. a 2-D Truss frame and an airplane wing. General applicability of the resulting optimization algorithm is then tested on two other benchmark problems, i.e. a 3-layer composite beam and a piezoelectric bimorph beam. Performance of the proposed algorithm is compared with several other competing algorithms, i.e. a fuzzy-GA-NN, a GA-NN, as well as a simple GA that only uses only FEA, in terms of both computational efficiency and accuracy. Statistical analysis indicates the superiority as well as robustness of the above approach as compared with the other optimization algorithms. Specifically, the proposed approach finds better structural designs more consistently while being computationally more efficient.     

基于粗糙集的多目标类Bayes粗糙集模型

介绍了两个目标类的决策信息系统中各目标类的Bayes粗糙集模型,并将这一模型推广到具有多目标类的情形.最后讨论了该模型的相关性质,计算实例表明该模型是有效的.     

多目标规划的Excel求解方法

多目标规划是由线性规划发展演变而来,多目标规划在建立模型后可以利用Excel求解,从而使多目标规划的求解变得简单易行.     

Robust design and optimization for autonomous PV-wind hybrid power systems

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.