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1.
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.  相似文献   

2.
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.  相似文献   

3.
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.  相似文献   

4.
Wang  Nenzi  Chang  Yau-Zen 《Tribology Letters》2004,17(2):119-128
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.  相似文献   

5.
面向大规模定制的装配线优化调度研究   总被引:6,自引:1,他引:5  
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。  相似文献   

6.
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。  相似文献   

7.
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.  相似文献   

8.
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.  相似文献   

9.
Pareto多目标遗传算法及其在机械健壮设计中的应用   总被引:8,自引:0,他引:8  
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,可能使设计成为不可行。本文提出了一种的基本设计变量敏感的健壮性设计方法,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 解健壮性问题。Pareto遗传算地可得到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个基本算予,选择、变异、交叉、  相似文献   

10.
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。  相似文献   

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