运输方式选择多目标优化问题的混合遗传算法

多式联运运输方式选择问题直接关系到货物运输的费用、时间和运输质量。首先分析了多式联运运输方式选择多目标优化问题的数学模型及虚拟运输网络图;其次,将基于信息熵的多属性决策方法引入适应度函数的设计中,提出了一种求解多式联运运输方式选择多目标优化问题的混合遗传算法,给出了染色体编码、遗传算子设计、染色体有效性判断和修正的方法;最后用示例对算法的有效性进行了验证。     

基于遗传算法的多式联运应急管理研究

实现在多式联运中实现运输时间和运输费用的最小化,多式联运运输方式选择问题直接关系到货物运输的费用和时间。首先分析了多式联运运输方式选择多目标优化问题的数学模型及虚拟运输网络图;其次,用遗传算法来解决多目标化问题,给出了染色体编码,遗传算子的设计,适应度函数定义;最后通过示例来演示,通过遗传算法来解决多式联运多目标优化的问题。实验表明,将此算法用于多式联运应急管理与传统算法相比,能加速进化速度和多角度寻优能力,提高应急决策。     

多式联运运输问题的混合遗传算法

多式联运运输方式的选择关系到货物运输所需费用、时间等。该文对需经过多式联运过程的运输问题进行了研究。首先分析了多式联运运输问题的数学模型;其次通过引入关于运输量及运输方式的混合编码,结合两种混合遗传算子,提出了一种求解多式联运运输问题的混合遗传算法;最后用数值例子对算法的有效性进行了验证。     

和声搜索算法优化多时间窗多式联运运输方案

针对多式联运运输路径上运输方式选择问题,考虑运输网络中多个节点存在服务时间窗的限制,建立了多个中间节点带软时间窗的多式联运运输方案优化模型,设计了一种基于字符编码方式的和声搜索算法,该算法采用新的和声生成方式及微调方式。仿真实例表明,所提算法与贪婪算法相比能获得具有更优运输总成本及不准点时间的运输方案。     

长江集装箱多式联运路径优化动态规划算法

考虑不同货流运输需求及其时间窗约束,研究长江集装箱多式联运路径优化问题,以运输总费用最小为目标构建数学模型,提出基于深度优先遍历的两阶段多式联运路径优化动态规划算法.第一阶段通过网络遍历提供所有货流可行路径方案集,作为第二阶段的输入完成多式联运路径优化.算例研究结果表明,动态规划算法可实现模型有效求解,适当调整时间窗约束或班次信息可降低多式联运费用.     

规模经济对多式联运网络成本最优模型的影响

在多式联运网路中,建立了一个基于规模经济的运输方式与车辆运力集成选择优化的模型。根据研究问题,规模经济体现在多式联运运输网络的货运量、运输距离和车辆运力上。通过多式联运运输网络成本最小化问题来表达该模型。用遗传算法可以找到合理的路线、运输方式和车辆运力。并通过一个案例对两种不同货物运输需求的情景进行了检验。启发式遗传算法的优化结果显示了规模经济在两种需求能力下,如何影响各种运输方式的物流总成本。此外,得出了两种情况下车辆安排的策略。     

求解多式联运运输方案选择的混合蛙跳算法

考虑多式联运路径上运输方案选择问题,建立了降低运输总成本和缩短运输总时间的多目标数学模型,通过加权目标函数,设计新的最差青蛙更新方式,提出一种基于字符编码方式的混合蛙跳算法,最后用示例验证了算法的有效性。     

多式联运中运输方式与运输路径集成优化模型研究*

运输方式和运输路径选择问题是影响多式联运时间和费用的关键问题,直接影响承运人和客户的利益。依据运输方式选择和运输路径优化的关系特点,采用主从混合智能启发式方法,构建了运输方式选择和运输路径优化集成模型,给出了粒子群—蚁群双层优化算法求解方案,解决了运输网络多节点、多方式、多路径的集成优化问题。实验结果表明,该方案优于蚁群算法和遗传算法。     

软时间窗多式联运4PL路径问题的改进乌鸦算法

针对机械故障、天气状况等随机因素在运输过程中易对各种运输方式造成影响,研究更具有实际意义的带软时间窗的多式联运4PL路径问题。在软时间窗约束下,以总运输费用最小为目标,建立带有软时间窗的多式联运4PL路径优化模型。设计基于天牛须搜索思想和莱维飞行机制的乌鸦搜索算法对模型进行求解,采用田口方法确定算法最优参数组合,与其他算法进行对比分析,实验结果表明改进算法具有更好的求解效果和稳定性。通过数据分析,采用多式联运的运输组织形式,相比单一3PL服务商的单一运输方式,能够有效降低总运输费用;对于客户不同的软时间窗要求,4PL集成商会确定不同的最优运送方案,并证实软时间窗的研究更具有实际意义。     

基于干扰度的长大货物联运路径规划方法

针对长大货物联运路径规划问题,构造干扰度函数以量化长大货物联运对正常运输的影响程度,并以长大货物联运总成本最少为第一优化目标,以对正常运输的干扰程度最低为第二优化目标,构建基于干扰度的长大货物联运路径多目标规划模型;基于研究问题的特征,结合所提类三棱柱网络构造算法,设计基于K-最短路的联运路径规划算法。算例结果表明,所提方法能制定多组长大货物联运路径规划方案,降低长大货物联运的干扰影响,能确定影响方案优劣的关键路段与节点。提出的方法可为长大货物联运组织提供决策支持。     

A flexible tolerance genetic algorithm for optimal problems with nonlinear equality constraints

A hybrid method called a flexible tolerance genetic algorithm (FTGA) is proposed in this paper to solve nonlinear, multimodal and multi-constraint optimization problems. This method provides a new hybrid strategy that organically merges a flexible tolerance method (FTM) into an adaptive genetic algorithm (AGA). AGA is to generate an initial population and locate the “best” individual. FTM, serving as one of the AGA operators, exploits the promising neighborhood individual by a search mechanism and minimizes a constraint violation of an objective function by a flexible tolerance criterion for near-feasible points. To evaluate the efficiency of the hybrid method, we apply FTGA to optimize four complex functions subject to nonlinear inequality and/or equality constraints, and compare these results with the results supplied by AGA. Numerical experiments indicate that FTGA can efficiently and reliably achieve more accurate global optima of complex, nonlinear, high-dimension and multimodal optimization problems subject to nonlinear constraints. Finally, FTGA is successfully implemented for the optimization design of a crank-toggle mechanism, which demonstrates that FTGA is applicable to solve real-world problems.     

A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing

The purpose of this paper is to develop a novel hybrid optimization method (HRABC) based on artificial bee colony algorithm and Taguchi method. The proposed approach is applied to a structural design optimization of a vehicle component and a multi-tool milling optimization problem.A comparison of state-of-the-art optimization techniques for the design and manufacturing optimization problems is presented. The results have demonstrated the superiority of the HRABC over the other techniques like differential evolution algorithm, harmony search algorithm, particle swarm optimization algorithm, artificial immune algorithm, ant colony algorithm, hybrid robust genetic algorithm, scatter search algorithm, genetic algorithm in terms of convergence speed and efficiency by measuring the number of function evaluations required.     

An interval algorithm for multi-objective optimization

This paper presents an interval algorithm for solving multi-objective optimization problems. Similar to other interval optimization techniques, [see Hansen and Walster (2004)], the interval algorithm presented here is guaranteed to capture all solutions, namely all points on the Pareto front. This algorithm is a hybrid method consisting of local gradient-based and global direct comparison components. A series of example problems covering convex, nonconvex, and multimodal Pareto fronts is used to demonstrate the method.     

物流配送中烟花算法结合遗传算法的异质车队路径优化方法

针对物流配送中车辆路径的问题,提出一种烟花算法结合遗传算法的物流配送异质车队路径优化方法。根据优先聚类其次路径的两阶段构造理论将新型群体智能算法烟花算法与遗传算法进行有效结合,首先按运力空间划分聚类区域,并采用改进的遗传算法解决为客户分配车辆的问题,然后通过采用烟花算法对路径排序实现本地路径优化。将该方法的实验结果与经验结果进行了比较,结果表明,所提出的混合算法模型得到的实验结果优于经验结果。     

A hybrid swarm intelligence algorithm for the travelling salesman problem

Abstract: We present a hybrid model named HRKPG that combines the random‐key search method and an individual enhancement scheme to thoroughly exploit the global search ability of particle swarm optimization. With a genetic algorithm, we can expand the area of exploration of individuals in the solution space. With the individual enhancement scheme, we can enhance the particle swarm optimization and the genetic algorithm for the travelling salesman problem. The objective of the travelling salesman problem is to find the shortest route that starts from a city, visits every city once, and finally comes back to the start city. With the random‐key search method, we can search the ability of the particle and chromosome. On the basis of the proposed hybrid scheme of HRKPG, we can improve solution quality quite a lot. Our experimental results show that the HRKPG model outperforms the particle swarm optimization and genetic algorithm in solution quality.     

A new hybrid optimization algorithm

We develop a new optimization algorithm that combines the genetic algorithm and a recently proposed global optimization algorithm called the nested partitions method. The resulting hybrid algorithm retains the global perspective of the nested partitions method and the local search capabilities of the genetic algorithm. We also present a detailed application of the new algorithm to a NP-hard product design problem and it is found empirically to outperform a pure genetic algorithm implementation, particularly for large problems.     

Reliability-based design optimization for problems with interval distribution parameters

Traditional reliability-based design optimization (RBDO) generally describes uncertain variables using random distributions, while some crucial distribution parameters in practical engineering problems can only be given intervals rather than precise values due to the limited information. Then, an important probability-interval hybrid reliability problem emerged. For uncertain problems in which interval variables are included in probability distribution functions of the random parameters, this paper establishes a hybrid reliability optimization design model and the corresponding efficient decoupling algorithm, which aims to provide an effective computational tool for reliability design of many complex structures. The reliability of an inner constraint is an interval since the interval distribution parameters are involved; this paper thus establishes the probability constraint using the lower bound of the reliability degree which ensures a safety design of the structure. An approximate reliability analysis method is given to avoid the time-consuming multivariable optimization of the inner hybrid reliability analysis. By using an incremental shifting vector (ISV) technique, the nested optimization problem involved in RBDO is converted into an efficient sequential iterative process of the deterministic design optimization and the hybrid reliability analysis. Three numerical examples are presented to verify the proposed method, which include one simple problem with explicit expression and two complex practical applications.