求解约束优化问题的改进布谷鸟搜索算法

为了提高约束优化问题的求解精度和收敛速度,提出求解约束优化问题的改进布谷鸟搜索算法。首先分析了基本布谷鸟搜索算法全局搜索和局部搜索过程中的不足,对其中全局搜索和局部搜索迭代公式进行重新定义,然后以一定概率在最优解附近进行搜索。对12个标准约束优化问题和4个工程约束优化问题进行测试并与多种算法进行对比,实验结果和统计分析表明所提算法在求解约束优化问题上具有较强的优越性。     

橡胶悬置元件结构参数优化设计方法

由于橡胶悬置元件的结构比较复杂,截面不规则,无法用传统的优化方法对其结构优化.利用遗传算法和神经网络相结合的策略对橡胶悬置元件的几何结构参数进行优化,即用神经网络学习算法建立橡胶悬置元件几何结构参数与其三个方向刚度的非线性全局映射关系,获得遗传算法求解结构优化问题所需的目标函数,用遗传算法进行优胜劣汰的寻优搜索运算,求出最优解.优化结果表明,橡胶悬置元件结构参数优化设计方法是可行的.     

浅谈计算机网络可靠优化计算过程中有效应用遗传算法

在计算机网络可靠度优化计算过程中,遗传算法以其自身简单的算法结构,高超的搜索效率,迅捷的求解速度和强大的实用性在全局计算中获得最优解的近似值,相对于传统算法,遗传算法在网络可靠度优化计算问题上有着明显的优势,计算速度得到前所未有的加快,计算效果因而得到有效优化,操作性能也大大提高。另外,遗传算法可以有效地完成降低网络成本的重要目标,并且在原有基础上将网络可靠度进一步提高,同时兼顾链路的介质成本问题、数学模型求解等问题。对计算机可靠度优化计算中遗传算法的有效应用问题进行分析与论证。     

基于0-1和序列二次规划的短期负荷优化调度

针对火电站机组的运行特性,使用多项式拟合机组煤耗特性函数并用最小二乘法求解各次项系数。考虑机组的运行状态,负荷平衡约束、出力和机组爬坡率限制,建立火电站的短期优化调度模型,首先采用穷举法或混沌遗传算法求解机组状态的可行解域,然后在可行解域内用序列二次规划算法求解机组的最优负荷分配,这样分两步求解的优点是在尽量保障最优解的前提下缩小问题求解的规模,从而减少计算时间。求解的结果与经验法对比并将其可视化可以明显发现本文算法的结果要优于经验法,实现了使用较少的燃煤实现输出相同的负荷的目的。并将本文算法应用于调控一体化平台的优化调度功能模块,为调控人员提供决策支持和参考。     

基于复合形算子的基础支护桩优化设计智能算法研究

本文通过遗传算法和传统复合形搜索法相结合,基于对遗传算法算子计算结构的调整,并将遗传算法与神经网络相结合,提出并研究了一种新的优化设计方法,协同求解复杂工程中的优化问题。并针对悬臂式支护桩的优化设计的数学模型,采用该算法进行了优化设计分析;计算结果表明,该算法可克服遗传算法最终进化至最优解较慢和人工神经网络易陷入局部解的缺陷,具有较好的全局性和收敛速度。     

一种基于新改进的Price算法的混合遗传算法求解约束优化问题

新改进的Price算法能够求解多峰、多维,以及不可微目标函数的全局优化问题.把新改进的Price算法作为局部搜索算子,并入到实数编码遗传算法中,构成一个混合遗传算法,求解约束优化问题.该混合算法增强了全局寻优能力,提高了函数值的精度,并减少了计算量.通过对13个约束标准测试函数的仿真实验,并和已有算法的比较,结果表明本文提出的混合遗传算法是有效的.     

基于混合粒子群算法的物流配送路径优化问题研究

针对物流配送路径优化问题,提出了一种融合Powell局部寻优算法和模拟退火算法的混合粒子群算法,以克服单用粒子群算法求解问题早熟收敛的不足,增加算法的开发能力,提高算法的全局搜索能力,并进行了实验计算.计算结果表明,用混合粒子群算法求解物流配送路径优化问题,可以在一定程度上提高粒子群算法在局部搜索能力和搜索全局最优解概率,从而得到质量较高的解.     

遗传算法在物流配送路径优化问题中的应用

遗传算法是一种基于自然进化原理的全局搜索随机算法。遗传算法在物流管理的运输问题、布局问题、选址问题、配送问题、调度问题等方面应用非常广泛。首先建立物流配送路径优化问题数学模型,在此基础上构造求解物流配送路径优化问题的遗传算法。用此遗传算法进行物流配送路径优化,可以方便有效地求得问题的最优解或近似最优解。     

具有时间窗约束累积性车辆路径问题的禁忌搜索优化算法

基于对中国实际物流运输中成本计算方法的研究,考虑到我国高速公路系统计价方式的特殊性,提出了具有时间窗约束的累积性车辆路径问题。以降低实际车辆运输成本为目标,设计了新型的禁忌搜索算法对问题进行有效求解;算法中嵌入多种邻域搜索方法,允许同时在可行和不可行解空间内进行邻域搜索,同时采用Nagata提出的时间窗违反量计算方法[1-2]对解的时间窗约束违反进行评估。针对提出的新型问题的数值试验证明了所采用的时间窗违反量计算方法的时间节约性和有效性;同时由于该问题可以覆盖传统的累积性车辆路径问题,对后者的数值实验以及与其他优化算法的对比验证了所提出算法的优良求解效果。     

复杂非线性函数最优化问题的一种实用智能算法

对浮点编码遗传算法加以改进,并与DFP变尺度算法相结合,经加速循环,构建新型混合加速遗传算法(以下简称NHAGA);协同求解具有变量边界约束的非凸、高度非线性的复杂函数最优化问题。算例测试表明,该法兼顾了改进浮点编码遗传算法全局搜索能力和DFP算法快速局部搜索能力的优点,成功搜索全局最优点的概率较高,是一种求解非凸、高度非线性全局优化问题的有效智能算法。     

基于退火算法的物流配送网的求优研究

模拟退火算法是一种启发式算法,是受到加热紧缩的退火过程所启发而提出来一种求解组合优化问题的一种逼近算法。算法要优于传统的贪婪算法,避免了陷入局部最优的可能,从而达到全局最优解。在物流配送网络中经常为有一些寻求最短路径等问题出现,为了能够达到最短、最优、最经济等,需要进行物流配送路径寻优。文中采用模拟退火算法进行一个示例的验证,效果证明可行。     

求解VRP问题的混沌模拟退火萤火虫算法

目的使萤火虫优化算法(GSO)能够适用于车辆路径问题(VRP)的求解,同时提高该算法的求解性能。方法通过对GSO算法的改进,提出求解VRP问题的混沌模拟退火萤火虫优化算法(CSAGSO)。首先,设计改进的GSO算法(IGSO)使IGSO算法能够适应VRP问题的求解;其次,在IGSO算法中引入模拟退火机制,提出模拟退火萤火虫优化算法(SAGSO),使IGSO算法可有效避免陷入局部极小并最终趋于全局最优。然后,在SAGSO算法中引入混沌机制,提出CSAGSO算法,对SAGSO算法的荧光素浓度值进行混沌初始化和混沌扰动;最后,对标准算例集进行仿真测试。结果与遗传算法、蚁群算法和粒子群算法相比,CSAGSO算法的全局寻优能力、收敛速度及稳定性均改善了50%以上。结论对GSO算法的改进是合理的,且CSAGSO算法的全局优化能力、收敛速度和稳定性均优于遗传算法、蚁群算法和粒子群算法。     

Engineering optimization using a real-parameter genetic-algorithm-based hybrid method

A hybrid optimization algorithm which combines the respective merits of the genetic algorithm and the simulated annealing algorithm is proposed. The proposed algorithm incorporates adaptive mechanisms designed to adjust the probabilities of the cross-over and mutation operators such that its hill-climbing ability towards the optimum solution is improved. The algorithm is used to optimize the weight of four planar or space truss structures and the results are compared with those obtained using other well-known optimization schemes. The evaluation trials investigate the performance of the algorithm in optimizing over discrete sizing variables only and over both discrete sizing variables and continuous configuration variables. The results show that the proposed algorithm consistently outperforms the other optimization methods in terms of its weight-saving capabilities. It is also shown that the global searching ability and convergence speed of the proposed algorithm are significantly improved by the inclusion of adaptive mechanisms to adjust the values of the genetic operators. Hence the hybrid algorithm provides an efficient and robust technique for solving engineering design optimization problems.     

Multidisciplinary design and optimization of an air launched satellite launch vehicle using a hybrid heuristic search algorithm

A multidisciplinary design and optimization strategy for a multistage air launched satellite launch vehicle comprising of a solid propulsion system to low earth orbit with the implementation of a hybrid heuristic search algorithm is proposed in this article. The proposed approach integrated the search properties of a genetic algorithm and simulated annealing, thus achieving an optimal solution while satisfying the design objectives and performance constraints. The genetic algorithm identified the feasible region of solutions and simulated annealing exploited the identified feasible region in search of optimality. The proposed methodology coupled with design space reduction allows the designer to explore promising regions of optimality. Modules for mass properties, propulsion characteristics, aerodynamics, and flight dynamics are integrated to produce a high-fidelity model of the vehicle. The objective of this article is to develop a design strategy that more efficiently and effectively facilitates multidisciplinary design analysis and optimization for an air launched satellite launch vehicle.     

A sequential quadratically constrained quadratic programming technique for a multi-objective optimization problem

In this article a line search algorithm is proposed for solving constrained multi-objective optimization problems. At every iteration of the proposed method, a subproblem is formulated using quadratic approximation of all functions. A feasible descent direction is obtained as a solution of this subproblem. This scheme takes care some ideas of the sequential quadratically constrained quadratic programming technique for single objective optimization problems. A non-differentiable penalty function is used to restrict constraint violations at every iterating point. Convergence of the scheme is justified under the Slater constraint qualification along with some reasonable assumptions. The proposed algorithm is verified and compared with existing methods with a set of test problems. It is observed that this algorithm provides better results in most of the test problems.     

用于结构优化设计的改进多目标群搜索算法

在多目标群搜索算法（multi-objective group search optimization, MGSO）基本原理的基础上,结合Pareto最优解理论,提出了基于约束改进的多目标群搜索算法（IMGSO）,并应用于多目标的结构优化设计．算法的改进主要有3个方面：第一,引入过渡可行域的概念来处理约束条件;第二,利用庄家法来构造非支配解集;最后,结合禁忌搜索算法和拥挤距离机制来选择发现者,以避免解集过早陷入局部最优,并提高收敛精度．采用IMGSO优化算法分别对平面和空间桁架结构进行了离散变量的截面优化设计,并与MGSO优化算法的计算结果进行了比较,结果表明改进的多目标群搜索优化算法IMGSO与MGSO算法相比具有更好的收敛精度.通过算例表明：IMGSO算法得到的解集中的解能大部分支配MGSO算法的解,在复杂高维结构中IMGSO算法的优越性更加明显,且收敛速度也有一定的提高,可有效应用于多目标的实际结构优化设计．     

Modified Differential Evolution Algorithm for Solving Dynamic Optimization with Existence of Infeasible Environments

Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over time. In such problems, it is commonly assumed that all problem instances are feasible. In reality some instances can be infeasible due to various practical issues, such as a sudden change in resource requirements or a big change in the availability of resources. Decision-makers have to determine whether a particular instance is feasible or not, as infeasible instances cannot be solved as there are no solutions to implement. In this case, locating the nearest feasible solution would be valuable information for the decision-makers. In this paper, a differential evolution algorithm is proposed for solving dynamic constrained problems that learns from past environments and transfers important knowledge from them to use in solving the current instance and includes a mechanism for suggesting a good feasible solution when an instance is infeasible. To judge the performance of the proposed algorithm, 13 well-known dynamic test problems were solved. The results indicate that the proposed algorithm outperforms existing recent algorithms with a margin of 79.40% over all the environments and it can also find a good, but infeasible solution, when an instance is infeasible.     

一种工业机器人多目标轨迹优化算法

为解决工业机器人工作效率低、能耗损失严重和关节冲击磨损较大的问题,提出了一种基于布谷鸟搜索(cuckoo search,CS)算法和非支配排序遗传算法-II(non-dominated sorting genetic algorithm-II,NSGA-II)的混合算法(简称为CSNSGA-II),用于机器人的轨迹优化...     

An alternating optimization approach for mixed discrete non-linear programming

This article contributes to the development of the field of alternating optimization (AO) and general mixed discrete non-linear programming (MDNLP) by introducing a new decomposition algorithm (AO-MDNLP) based on the augmented Lagrangian multipliers method. In the proposed algorithm, an iterative solution strategy is proposed by transforming the constrained MDNLP problem into two unconstrained components or units; one solving for the discrete variables, and another for the continuous ones. Each unit focuses on minimizing a different set of variables while the other type is frozen. During optimizing each unit, the penalty parameters and multipliers are consecutively updated until the solution moves towards the feasible region. The two units take turns in evolving independently for a small number of cycles. The validity, robustness and effectiveness of the proposed algorithm are exemplified through some well known benchmark mixed discrete optimization problems.     

An efficient and practical approach to obtain a better optimum solution for structural optimization

For many structural optimization problems, it is hard or even impossible to find the global optimum solution owing to unaffordable computational cost. An alternative and practical way of thinking is thus proposed in this research to obtain an optimum design which may not be global but is better than most local optimum solutions that can be found by gradient-based search methods. The way to reach this goal is to find a smaller search space for gradient-based search methods. It is found in this research that data mining can accomplish this goal easily. The activities of classification, association and clustering in data mining are employed to reduce the original design space. For unconstrained optimization problems, the data mining activities are used to find a smaller search region which contains the global or better local solutions. For constrained optimization problems, it is used to find the feasible region or the feasible region with better objective values. Numerical examples show that the optimum solutions found in the reduced design space by sequential quadratic programming (SQP) are indeed much better than those found by SQP in the original design space. The optimum solutions found in a reduced space by SQP sometimes are even better than the solution found using a hybrid global search method with approximate structural analyses.