Dynamic balancing of planar mechanisms using genetic algorithm

This paper presents an optimization technique to dynamically balance the planar mechanisms in which the shaking forces and shaking moments are minimized using the genetic algorithm (GA). A dynamically equivalent system of point-masses that represents each rigid link of a mechanism is developed to represent link’s inertial properties. The shaking force and shaking moment are then expressed in terms of the point-mass parameters which are taken as the design variables. These design variables are brought into the optimization scheme to reduce the shaking force and shaking moment. This formulates the objective function which optimizes the mass distribution of each link. First, the problem is formulated as a single objective optimization problem for which the genetic algorithm produces better results as compared to the conventional optimization algorithm. The same problem is then formulated as a multi-objective optimization problem and multiple optimal solutions are created as a Pareto front by using the genetic algorithm. The masses and inertias of the optimized links are computed from the optimized design variables. The effectiveness of the proposed methodology is shown by applying it to a standard problem of four-bar planar mechanism available in the literature.     

A multi-objective genetic algorithm for scheduling in flow shops to minimize the makespan and total flow time of jobs

In this paper the problem of permutation flow shop scheduling with the objectives of minimizing the makespan and total flow time of jobs is considered. A Pareto-ranking based multi-objective genetic algorithm, called a Pareto genetic algorithm (GA) with an archive of non-dominated solutions subjected to a local search (PGA-ALS) is proposed. The proposed algorithm makes use of the principle of non-dominated sorting, coupled with the use of a metric for crowding distance being used as a secondary criterion. This approach is intended to alleviate the problem of genetic drift in GA methodology. In addition, the proposed genetic algorithm maintains an archive of non-dominated solutions that are being updated and improved through the implementation of local search techniques at the end of every generation. A relative evaluation of the proposed genetic algorithm and the existing best multi-objective algorithms for flow shop scheduling is carried by considering the benchmark flow shop scheduling problems. The non-dominated sets obtained from each of the existing algorithms and the proposed PGA-ALS algorithm are compared, and subsequently combined to obtain a net non-dominated front. It is found that most of the solutions in the net non-dominated front are yielded by the proposed PGA-ALS.     

A two-stage genetic algorithm for large size job shop scheduling problems

The majority of large size job shop scheduling problems are non-polynomial-hard (NP-hard). In the past few decades, genetic algorithms (GAs) have demonstrated considerable success in providing efficient solutions to many NP-hard optimization problems. But there is no literature available considering the optimal parameters when designing GAs. Unsuitable parameters may generate an inadequate solution for a specific scheduling problem. In this paper, we proposed a two-stage GA which attempts to firstly find the fittest control parameters, namely, number of population, probability of crossover, and probability of mutation, for a given job shop problem with a fraction of time using the optimal computing budget allocation method, and then the fittest parameters are used in the GA for a further searching operation to find the optimal solution. For large size problems, the two-stage GA can obtain optimal solutions effectively and efficiently. The method was validated based on some hard benchmark problems of job shop scheduling.     

Application of the Genetic Algorithm to the Multi-Objective Optimization of Air Bearings

A feasible solution must be obtained in a reasonable time with high probability of global optimum for a complex tribological design problem. To meet this decisive requirement in a multi-objective optimization problem, the popular and powerful genetic algorithms (GAs) are adopted in an illustrated air bearing design. In this study, the goal of multi-objective optimization is achieved by incorporating the criterion of Pareto optimality in the selection of mating groups in the GAs. In the illustrated example the diversity of group members in the evolution process is much better maintained by using Pareto ranking method than that with the roulette wheel selection scheme. The final selection of the optimal point of the points satisfied the Pareto optimality is based on the minimum–maximum objective deviation criterion. It is shown that the application of the GA with the Pareto ranking is especially useful in dealing with multi-objective optimizations. A hybrid selection scheme combining the Pareto ranking and roulette wheel selections is also presented to deal with a problem with a combined single objective. With the early generations running the Pareto ranking criterion, the resultant divergence preserved in the population benefits the overall GA's performance. The presented procedure is readily adoptable for parallel computing, which deserves further study in tribological designs to improve the computational efficiency.     

基于网格支配的微型多目标遗传算法

提出了一种基于网格支配的微型多目标遗传算法,该算法在求解较多目标函数的优化问题时具有较好的收敛性和较高的计算效率。该算法引入网格支配概念并结合微型多目标遗传算法,在每一代进化种群中计算各个个体的网格值、网格拥挤距离和网格坐标点距离,根据网格支配分级和网格选择机制策略选取精英个体,并对其进行交叉和变异操作,使其朝前沿面收敛以获得Pareto最优解。4个测试函数和2个工程实例验证了该算法的有效性。     

面向大规模定制的装配线优化调度研究

针对大规模定制生产模式下汽车装配线调度存在的问题,提出一种多目标优化调度的方法,设计了相应的目标函数。提出一种多目标遗传算法,设计了相应的编码、选择和交换方案,在算法实现中对精英策略和选择机制进行了改进。仿真实验说明该算法可行有效,优于VEGA、PGA和NPGA等其他遗传算法。     

基于遗传算法的行星轮减速器优化

遗传算法可以通过模拟自然进化的过程来搜索最优解,只需目标函数及适应度函数,不需要依赖梯度或其它辅助信息,多用于函数优化、组合优化、生产调度、自动控制、机器人学、图像处理、遗传编程等诸多方面。文中利用遗传算法对行星轮减速器进行了多目标函数的遗传算法优化分析。     

Optimal design of a squeeze film damper using an enhanced genetic algorithm

This paper represents that an enhanced genetic algorithm (EGA) is applied to optimal design of a squeeze film damper (SFD) to minimize the maximum transmitted load between the bearing and foundation in the operational speed range. A general genetic algorithm (GA) is well known as a useful global optimization technique for complex and nonlinear optimization problems. The EGA consists of the GA to optimize multi-modal functions and the simplex method to search intensively the candidate solutions by the GA for optimal solutions. The performance of the EGA with a benchmark function is compared to them by the IGA (Immune-Genetic Algorithm) and SQP (Sequential Quadratic Programming). The radius, length and radial clearance of the SFD are defined as the design parameters. The objective function is the minimization of a maximum transmitted load of a flexible rotor system with the nonlinear SFDs in the operating speed range. The effectiveness of the EGA for the optimal design of the SFD is discussed from a numerical example.     

Bat intelligence search with application to multi-objective multiprocessor scheduling optimization

In this paper, we present the bat intelligence search for the first time. Bat intelligence is a novel and unique heuristic that models two major prey hunting behaviors of bats: (a) utilization of echolocation to observe the environment and (b) employment of constant absolute target direction approach to pursue preys. In order to illustrate the performance of bat intelligence, we implement this heuristic to solve two types of multiprocessor scheduling problems (MSP): single objective MSP and multi-objective MSP. In single objective MSP, we independently solve for minimization of makespan and minimization of tardiness. In multiple objective MSP, these two objectives are optimized simultaneously. In the single objective MSP, on average, the bat intelligence outperformed the list algorithm and the genetic algorithm by 11.12% when solving for minimization of makespan and by 23.97% when solving for the minimization of tardiness. In comparison to the genetic algorithm, the bat intelligence produces better results for the same computational effort. In multiple objective MSP, bat intelligence is combined with normalized weighted additive utility function to generate a set of efficient solutions by varying the weights of importance. The results demonstrate that the bat intelligence finds a set of Pareto optimal solutions on bi-objective optimization of MSP.     

基于遗传算法和分枝定界的多车间空闲产能调度方法

个性化产品具有多变的产品结构和复杂的加工特征,使得单一车间难以满足如此广泛的加工参数,需要借助外协车间才能完成生产任务。每个外协车间负载不同,空闲时段也不同,为了提升这些时间的利用率,提出基于遗传算法和分枝定界的混合调度方法。设计基于混合优化策略的动态重调度机制,将动态的生产过程转化为一系列在时间上连续的静态调度问题;建立以最小化总拖期为目标的数学模型;采用遗传算法和分枝定界方法对调度过程中的两个阶段分别进行优化,即在每个事件时刻采用遗传算法生成预调度方案并划分为已派工部分、待派工部分和可调整部分,在已派工部分正在执行的时间段采用分枝定界方法对可调整部分进行改进优化。采用运筹学优化器OR-Tools验证所提模型的正确性。试验数据表明,与单一方法相比,混合方法在所有实例上获得改进,验证了所提方法是有效可行的。     

Pareto多目标遗传算法及其在机械健壮设计中的应用

在机械或结构的优化设计中 ,普遍存在约束的作用 ,且最优解往往位于可行域的边界上。由于外界环境的变化或人为因素造成设计变量扰动 ,可能使设计成为不可行。本文提出了一种的基于设计变量敏感性的健壮性设计方法 ,并提出了一种用 Pareto遗传算法来实施的带约束的多目标优化方法以求解健壮性问题。 Pareto遗传算法可得到 Pareto最优解集 ,从中可选出满足设计需要的解。本文提出的算法包括 5个基本算子 :选择、变异、交叉、小生境技术、Pareto集合过滤器。文中用算例说明该方法的应用     

GA-based synthesis approach for machining scheme selection and operation sequencing optimization for prismatic parts

To obtain global and near-global optimal process plans based on the combinations of different machining schemes selected from each feature, a genetic algorithm-based synthesis approach for machining scheme selection and operation sequencing optimization is proposed. The memberships derived from the fuzzy logic neural network (FL-NN), which contains the membership function of each machining operation to batch size, are presented to determine the priorities of alternative machining operations for each feature. After all alternative machining schemes for each feature are generated, their memberships are obtained by calculation. The proposed approach contains the outer iteration and nested genetic algorithm (GA). In an outer iteration, one machining scheme for each feature is selected by using the roulette wheel approach or highest membership approach in terms of its membership first, and then the corresponding operation precedence constraints are generated automatically. These constraints, which can be modified freely in different outer iterations, are then used in a constraints adjustment algorithm to ensure the feasibility of process plan candidates generated in GA. After that, GA obtains an optimal process plan candidate. At last, the global and near-global optimal process plans are obtained by comparing the optimal process plan candidates in the whole outer iteration. The proposed approach is experimentally validated through a case study.     

解决U形装配线平衡调度问题的免疫协同进化算法

研究了混流U形装配线平衡与调度的多目标集成优化问题,提出了一种基于Pareto最优的多目标克隆免疫协同进化算法。该算法以两个单克隆抗体群对应平衡与调度两个子问题,分别编码并协同进化,以一个多克隆抗体群保存最优完整解并采取精英策略,使得子种群间既存在协作也存在竞争。提出从抗体的基因型和表现型同时评价抗体亲和度,并改进了共生伙伴选择机制以提高算法的收敛性能。仿真实例证明算法有着更快的收敛速度且比单种群进化算法更适于U形装配线平衡调度问题的求解。     

资源约束下产品开发任务调度的多目标优化

鉴于产品开发任务调度过程中存在资源约束问题和学习与遗忘效应,需要对多个目标进行优化决策,通过定义资源平均利用率并提出学习遗忘效应矩阵,结合耦合设计的多阶段迭代模型,以各阶段资源利用率为约束条件,建立资源约束下考虑学习与遗忘效应的任务调度时间与成本的多目标优化数学模型。采用带精英策略的非支配排序遗传算法求解得出Pareto最优解集,并采用改进的多目标理想点法对该解集进行选优,得到最优任务调度方案。以某电动汽车的开发过程为例,验证了该优化模型能够减小产品开发时间,降低产品开发成本,提高总资源利用率。     

A Pareto evolutionary algorithm approach to bi-objective unrelated parallel machine scheduling problems

This paper addresses the unrelated parallel machine scheduling problem with job sequence- and machine-dependent setup times. The preemption of jobs is not permitted, and the optimization criteria are to simultaneously minimize total weighted flow time and total weighted tardiness. The problem has applications in industries such as TFT-LCD, automobile, and textile manufactures. In this study, a Pareto evolutionary approach is proposed to solve the bi-objective scheduling problem. The performance of this approach using different encoding and decoding schemes is evaluated and is compared with that of two multi-objective simulated annealing algorithms via a set of instances generated by a method in the literature. The experimental results indicate that the Pareto evolutionary approach using random key representation and weighted bipartite matching optimization method outperforms the other algorithms in terms of closeness metric, based on similar computation times. Additionally, although the proposed method does not provide the best distribution in terms of diversity metric, it found most of the reference solutions.     

Multidimensional visualization and clustering for multiobjective optimization of artificial satellite heat pipe design

This study presents a newly developed approach for visualization of Pareto and quasi-Pareto solutions of a multiobjective design problem for the heat piping system in an artificial satellite. Given conflicting objective functions, multiobjective optimization requires both a search algorithm to find optimal solutions and a decision-making process for finalizing a design solution. This type of multiobjective optimization problem may easily induce equally optimized multple solutions such as Pareto solutions, quasi-Pareto solutions, and feasible solutions. Here, a multidimensional visualization and clustering technique is used for visualization of Pareto solutions. The proposed approach can support engineering decisions in the design of the heat piping system in artificial satellites. Design considerations for heat piping system need to simultaneously satisfy dual conditions such as thermal robustness and overall limitation of the total weight of the system. The proposed visualization and clustering technique can be a valuable design tool for the heat piping system, in which reliable decision-making has been frequently hindered by the conflicting nature of objective functions in conventional approaches.     

基于降维的驾驶员侧约束系统高维目标优化

为解决将高维目标变为单目标优化时各子目标不能同时较优,而多目标算法直接用于高维目标优化时又存在难以找到一个有代表性的Pareto非劣解集问题,在某轿车驾驶员侧约束系统的优化过程中提出了乘员损伤准则与多目标算法协同优化的方法。在已有相关损伤准则基础上根据最新版的FMVSS 208和ECE R94法规提出了适合研究问题的损伤准则;以提出的损伤准则为媒介,将一个高维目标优化问题降为一个低维目标优化问题,通过灵敏度分析、实验设计、多项式近似模型筛选出优化设计变量并得到近似模型,用多目标算法NSGA-Ⅱ对近似模型进行计算得到Pareto非劣解集,将得到的Pareto非劣解集中的每个解代入损伤准则损伤值计算公式,升序排列得到各子目标同时较优而损伤值最小的优化解。最终的优化结果表明：该方法很好地解决了乘员约束系统的高维目标优化问题,优化效果明显。     

Artificial neural network based on genetic algorithm for emissions prediction of a SI gasoline engine

This paper proposes a hybrid learning of artificial neural network (ANN) with the nondominated sorting genetic algorithm-II (NSGAII) to improve accuracy in order to predict the exhaust emissions of a four stroke spark ignition (SI) engine. In the proposed approach, the genetic algorithm (GA) determines initial weights of local linear model tree (LOLIMOT) neural networks. A multi-objective optimization problem is determined. A sensitivity analysis is performed on NSGA-II parameters in order to provide better solutions along the optimal Pareto front. Then, a fuzzy decision maker and the technique for order preference by similarity to ideal solution (TOPSIS) are employed to select compromised solutions among the obtained Pareto solutions. The LOLIMOT-GA responses are compared with the provided by radial basis function (RBF) and multilayer perceptron (MLP) neural networks in terms of correlation coefficient R ².     

Multi-objective integrated optimization research on preventive maintenance planning and production scheduling for a single machine

Preventive maintenance (PM) planning and production scheduling are among the most important problems in the manufacturing industries. Researchers have begun to investigate the integrated optimization problem of PM and production scheduling with a single objective. However, many industries have trade-offs in their scheduling problems where multiple objectives must be considered in order to optimize the overall performance of the system. In this paper, five objectives, including minimizing maintenance cost, makespan, total weighted completion time of jobs, total weighted tardiness, and maximizing machine availability are simultaneously considered to optimize the integrated problem of PM and production scheduling introduced by Cassady and Kutanoglu. Multi-objective genetic algorithm (MOGA) is used to solve the integrated optimization problem. To illuminate the conflicting nature of the objective functions, decision-makers’ preferences of the multiple objectives are not integrated into the MOGA. The total weighted percent deviation, which represents not only the preferences of the objectives but also the deviations of the solutions, is proposed to help decision-makers select the best solution among the near-Pareto optimal solutions obtained by the MOGA. A numerical example reveals the necessity and significance of integrating optimization of PM and production scheduling considering multiple objectives.     

Pareto archive particle swarm optimization for multi-objective fuzzy job shop scheduling problems

This paper addresses multi-objective job shop scheduling problems with fuzzy processing time and due-date in such a way to provide the decision-maker with a group of Pareto optimal solutions. A new priority rule-based representation method is proposed and the problems are converted into continuous optimization ones to handle the problems by using particle swarm optimization. The conversion is implemented by constructing the corresponding relationship between real vector and the chromosome obtained with the new representation method. Pareto archive particle swarm optimization is proposed, in which the global best position selection is combined with the crowding measure-based archive maintenance, and the inclusion of mutation into the proposed algorithm is considered. The proposed algorithm is applied to eight benchmark problems for the following objectives: the minimum agreement index, the maximum fuzzy completion time and the mean fuzzy completion time. Computational results demonstrate that the proposal algorithm has a promising advantage in fuzzy job shop scheduling.