多品种柔性生产企业的订单调度模型及其遗传算法

以订单总完工时间最小和订单平均流程时间最小为目标函数,利用改进的多目标遗传算法生成了多品种订单调度模型.为解决组合模型的指数爆炸问题,提出了一种按规则分配订单以及订单中各作业排序相结合的集成调度思想;以一种整数和字母组合的编码方法用于可行解的表达,并在每个分目标的进化过程中,对选择、交叉、变异算子以及精英解保留策略重新进行设计,保证了解的分布性和均匀性;同时还提出了一种新的终止条件,将精英种群与分目标的子种群进行合并,从而加快收敛的速度.以典型的订单生产企业为例进行仿真实验,实验结果表明,应用该算法可以获得满意的Pareto解集.     

求解约束优化问题的一种新的遗传算法

提出一种新的求解约束优化问题的遗传算法,算法通过重新定义可行解与不可行解的适应度函数分别对它们进行选择,有效避免了惩罚函数法引入参数所带来的困难,重新设计的交叉算子使得算法对解空间的寻优范围扩大了.数值实验结果表明算法具有较好的鲁棒性,且对最优解位于约束边界上的一类问题具有很大优势.     

基于支配强度的NSGA Ⅱ改进算法在研究生招生面试分组中的应用

针对研究生招生面试分组这一NP难问题,提出了一种以分组遗传算法(GGA)和基于支配强度的改进NSGA Ⅱ算法为基础的混合多目标分组遗传算法。通过基于矩阵编码的多交叉/多变异算子、次精英化的初始化种群策略以及改进的帕累托支配关系,解决了经典NSGA Ⅱ算法在该问题中的收敛速度慢、易陷入局部最优的问题。仿真实验结果表明,该方法只需进行较少代数(不超过100代)的进化,即可获得最优解集,满足了快速分组的用户偏好。     

并列选择单亲遗传算法在自动化立体仓库货位优化中的应用

针对传统遗传算法在求解自动化立体仓库货位优化多目标模型中容易陷于局部最优解以及交叉变异过程中产生大量不可行解等问题,提出了并列选择单亲遗传算法.算法采用了0,1矩阵编码、并列选择算子、单亲变异算子等,有效避免了交叉变异操作产生不可行解的问题.通过对控制参数进行较合理地选取,算法能够综合考虑各子目标的相对优秀个体,从中选取出全局近似最优解,有效降低了算法陷于局部最优解的概率.利用该算法对36种货物的自动化立体仓库货位进行优化,通过比较优化前后的货位对应的拣选时间及货架重心可以看出,优化后的货位对应的拣选效率及货架稳定性均有明显提高.     

基于遗传算法的多目标柔性工作车间调度问题求解

本文针对柔性工作车间调度问题给出了一个有意义的综合目标尽可能缩短制造周期的同时尽可能的减少机器负荷。由于传统遗传算法在多目标柔性工作车间调度问题上的局限性，我们提出了一种改进遗传算法：首先，我们给出了针对综合目标的工序调度算法获得初始集合；接着，针对柔性工作车间调度问题的特点，我们在常用的基于工序顺序的编码方法上融入了基于机器分配的编码方法，并据此设计了相应的交叉变异操作；最后借鉴了物种进化现象中的环境迁移思想设计了解决多目标优化问题的迁移操作。实验结果表明，改进的遗传算法在多目标柔性工作车间调度问题的解决上要优于传统遗传算法。     

油田注水系统拓扑布局优化的混合遗传算法

以投资最小为目标函数,建立了注水系统拓扑布局优化数学模型.根据模型特点,将优化问题分为两层,分别采用遗传算法和非线性优化方法进行求解.并对遗传算法的操作过程进行了改进,调整了适应函数,改进了交叉和变异操作,结合了模拟退火算法,在操作过程中使约束条件得到满足,减少了不可行解的产生,使遗传算法的优化性能得到了提高.优化算例说明了该方法的有效性.     

二层线性规划的自适应遗传算法

提出了一种自适应遗传算法来求解二层线性规划问题.该方法克服了难以确定合适的交叉概率和变异概率的困难.另外,在该方法中还采用了其它一些技巧不仅解决了在采用遗传算法经常出现的有些个体不可行的问题,而且还改进了算法的效率.     

工序顺序柔性的作业车间调度问题的改进遗传算法求解

针对在工艺设计中提供工序顺序柔性的作业车间调度问题,总结了该问题中柔性工序顺序的类型和特点,并提出了一种求解该问题的改进遗传算法.以尽可能缩短制造周期为目标,结合问题特点,改进了染色体的编码方式,在常用的基于工序顺序的编码方法上融入了基于柔性工序顺序的编码方法,并据此设计了相应的交叉、变异等操作,防止遗传过程中不可行解的产生,避免染色体修复,提高求解效率.最后以MATLAB为工具用某轴承公司的实际生产数据对该算法进行了仿真.通过与不考虑工序顺序柔性的作业车间调度问题遗传算法求解结果进行对比,证明了该算法可行性和有效性.     

解非线性规划问题的非参数罚函数多目标正交遗传算法

对非线性规划问题的处理通常采用罚函数法,使用罚函数法的困难在于参数的选取.本文提出了一种解非线性规划问题非参数罚函数多目标正交遗传算法,对违反约束的个体进行动态的惩罚以保持群体中不可行解的一定比例,从而不但有效增加种群的多样性,而且避免了传统的过度惩罚缺陷,使群体更好地向最优解逼近.数据实验表明该算法对带约束的非线性规划问题求解是非常有效的.     

分层混合局部搜索策略异构多核系统调度

针对遗传算法解决异构多核系统的任务调度问题容易产生早熟现象及其局部寻优能力较差的缺点,将局部搜索算法与遗传算法相结合,创新性地提出一种求解异构多核系统的任务调度问题的分层混合局部搜索遗传算法。该算法提出一种新的分层优化策略以产生初始种群,在变异操作中,对部分个体设计3-opt优化变异,对种群中的优秀个体用改进的Lin-Kernighan算法进行优化。仿真实验结果表明,分层混合局部搜索遗传算法求解异构多核系统的任务调度问题时可以高效获得高质量的解。     

基于种群分类排序的约束优化遗传算法

针对约束优化问题,提出了一类将种群中的个体分类排序的思想.算法的特点在于:先将种群中的解分为可行解和不可行解两类,然后分别按照不同的标准排序.由于很多约束优化问题的最优解位于可行域的边界上或附近,所以排序时并不认为可行解一定优于不可行解.基于此分类排队思想,特别设计了只允许同等级个体进行交叉的新的交叉算子,称之为同等级交叉算子,以及基于一维搜索的变异算子.算法同时采用了保证固定比例不可行解的自适应策略.4个标准测试函数的数值仿真结果验证了算法的有效性.     

改进遗传算法优化非线性规划问题

针对遗传算法在处理优化问题上的独特优势,主要研究遗传算法的改进,并将其应用于优化非线性规划问题.在进化策略上,采用群体精英保留方式,将适应度值低的个体进行变异;交叉算子采用按决策变量分段交叉方式,提高进化速度;在优化有约束非线性规划问题时,引入算子修正法,对非可行个体进行改善.MATLAB仿真实验表明,方法是一种有效的、可靠的、方便的方法.     

Network design problem with relays: A genetic algorithm with a path-based crossover and a set covering formulation

The network design problem with relays arises in telecommunications and distribution systems where the payload must be reprocessed at intermediate stations called relays on the route from its origin to its destination. In fiber-optic networks, for example, optical signals may be regenerated several times to overcome signal degradation because of attenuation and other factors. Given a network and a set of commodities, the network design problem with relays involves selecting network edges, determining a route for each commodity, and locating relays to minimize the network design cost. This paper presents a new formulation to the problem based on set covering constraints. The new formulation is used to design a genetic algorithm with a specialized crossover/mutation operator which generates a feasible path for each commodity, and the locations of relays on these paths are determined by solving the corresponding set covering problem. Computational experiments show that the proposed approach can outperform other approaches, particularly on large size problems.     

改进遗传算法求解TSP问题

提出了一种改进遗传算法求解 TSP.该方法在迭代初期引入不适应度函数作为评价标准 ,结合启发式交叉和边重组交叉算子设计了一种新的交叉算子 ,并对变异后个体进行免疫操作 .此外对操作后群体进行整理 ,删除群体中相同个体 ,得到规模为 N1的中间群体 ,对较优的 N -N 1个个体进行启发式变异 ,并将变异后个体补充进中间群体 ,生成规模为 N的新群体 ,这样保证群体中没有相同个体 ,从而保证群体多样性 .数值结果表明这种改进遗传算法是有效的 .     

A family of genetic algorithms for the pallet loading problem

This paper is concerned with a family of genetic algorithms for the pallet loading problem. Our algorithms differ from previous applications of genetic algorithms to two-dimensional packing problems in that our coding contains all the information needed to produce the packing it represents, rather than relying on a packing algorithm to decode each individual solution. We experiment with traditional one-dimensional string representations, and a two-dimensional matrix representation which preserves the notion of closeness between positions on the pallet. Two new crossover operators are introduced for the two-dimensional case. Our definition of solution space includes both feasible and infeasible solutions and we suggest a number of different fitness functions which penalise infeasibility in different ways and a repair operator which allows our populations to maintain feasibility. The results of experiments designed to test the effectiveness of these features are presented.     

A genetic algorithm for the generalised assignment problem

A new algorithm for the generalised assignment problem is described in this paper. The algorithm is adapted from a genetic algorithm which has been successfully used on set covering problems, but instead of genetically improving a set of feasible solutions it tries to genetically restore feasibility to a set of near-optimal ones. Thus it may be regarded as operating in a dual sense to the more familiar genetic approach. The algorithm has been tested on generalised assignment problems of substantial size and compared to an exact integer programming approach and a well-established heuristic approach.     

Optimised crossover genetic algorithm for capacitated vehicle routing problem

This paper presents a genetic algorithm for solving capacitated vehicle routing problem, which is mainly characterised by using vehicles of the same capacity based at a central depot that will be optimally routed to supply customers with known demands. The proposed algorithm uses an optimised crossover operator designed by a complete undirected bipartite graph to find an optimal set of delivery routes satisfying the requirements and giving minimal total cost. We tested our algorithm with benchmark instances and compared it with some other heuristics in the literature. Computational results showed that the proposed algorithm is competitive in terms of the quality of the solutions found.     

The use of genetic algorithms to solve the economic lot size scheduling problem

The purpose of this paper is to investigate the use genetic algorithms (GAs) for solving the Economic Lot Size Scheduling Problem (ELSP). The ELSP is formulated using the Basic Period (BP) approach which results in a problem having one continuous decision variable and a number of integer decision variables equal to the number of products being produced. This formulation is ideally suited for using GAs. The GA is tested on Bomberger's classical problem. The resulting solutions were better than those obtained using an iterative dynamic programming (DP) approach. The total cost of GA solutions to the problem with utilization up to 65% were within 3.4% of the lower bound. The GA also performed well for higher utilization yielding solutions within 13.87% of the lower bound for utilization up to 86%. The GA was tested on a 30-item problem and good solutions were obtained. The results of the GA under different binary representations, crossover methods, and initialization methods are compared to identify the best settings. The results indicate that for this particular problem, binary representation works better than Gray coding, 2-point crossover is best, and an infeasible starting population is better than feasible.     

Nature inspired genetic algorithms for hard packing problems

This paper presents two novel genetic algorithms (GAs) for hard industrially relevant packing problems. The design of both algorithms is inspired by aspects of molecular genetics, in particular, the modular exon-intron structure of eukaryotic genes. Two representative packing problems are used to test the utility of the proposed approach: the bin packing problem (BPP) and the multiple knapsack problem (MKP). The algorithm for the BPP, the exon shuffling GA (ESGA), is a steady-state GA with a sophisticated crossover operator that makes maximum use of the principle of natural selection to evolve feasible solutions with no explicit verification of constraint violations. The second algorithm, the Exonic GA (ExGA), implements an RNA inspired adaptive repair function necessary for the highly constrained MKP. Three different variants of this algorithm are presented and compared, which evolve a partial ordering of items using a segmented encoding that is utilised in the repair of infeasible solutions. All algorithms are tested on a range of benchmark problems, and the results indicate a very high degree of accuracy and reliability compared to other approaches in the literature.