Optimal design of nonlinear squeeze film damper using hybrid global optimization technique

The optimal design of the squeeze film damper (SFD) for rotor system has been studied in previous researches. However, these researches have not been considering jumping or nonlinear phenomena of a rotor system with SFD. This paper represents an optimization technique for linear and nonlinear response of a simple rotor system with SFDs by using a hybrid GA-SA algorithm which combined enhanced genetic algorithm (GA) with simulated annealing algorithm (SA). The damper design parameters are the radius, length and radial clearance of the damper. The objective function is to minimize the transmitted load between SFD and foundation at the operating and critical speeds of the rotor system with SFD which has linear and nonlinear unbalance responses. The numerical results show that the transmitted load of the SFD is greatly reduced in linear and nonlinear responses for the rotor system.     

Intelligent Optimization Methods for the Design of an Overhead Travelling Crane

In design optimization of crane metal structures, present approaches are based on simple models and mixed variables, which are difficult to use in practice and usually lead to failure of optimized results for rounding variables. Crane metal structure optimal design(CMSOD) belongs to a constrained nonlinear optimization problem with discrete variables. A novel algorithm combining ant colony algorithm with a mutation-based local search(ACAM) is developed and used for a real CMSOD for the first time. In the algorithm model, the encoded mode of continuous array elements is introduced. This not only avoids the need to round optimization design variables during mixed variable optimization, but also facilitates the construction of heuristic information, and the storage and update of the ant colony pheromone. Together with the proposed ACAM, a genetic algorithm(GA) and particle swarm optimization(PSO) are used to optimize the metal structure of a crane. The optimization results show that the convergence speed of ACAM is approximately 20% of that of the GA and around 11% of that of the PSO. The objective function value given by ACAM is 22.23% less than the practical design value, a reduction of 16.42% over the GA and 3.27% over the PSO. The developed ACAM is an effective intelligent method for CMSOD and superior to other methods.     

Selection of optimal cutting conditions for pocket milling using genetic algorithm

Pocket milling is the most known machining operation in the domains of aerospace, die, and mold manufacturing. In the present work, GA-OptMill, a genetic algorithm (GA)-based optimization system for the minimization of pocket milling time, is developed. A wide range of cutting conditions, spindle speed, feed rate, and axial and radial depth of cut, are processed and optimized while respecting the important constraints during high-speed milling. Operational constraints of the machine tool system, such as spindle speed and feed limits, available spindle power and torque, acceptable limits of bending stress and deflection of the cutting tool, and clamping load limits of the workpiece system, are respected. Chatter vibration limits due to the dynamic interaction between cutting tool and workpiece are also embedded in the developed GA-OptMill system. Enhanced capabilities of the system in terms of encoded GA design variables and operators, targeted cutting conditions, and constraints are demonstrated for different pocket sizes. The automatically identified optimal cutting conditions are also verified experimentally. The developed optimization system is very appealing for industrial implementation to automate the selection of optimal cutting conditions to achieve high productivity.     

Integrating Box-Behnken design with genetic algorithm to determine the optimal parametric combination for minimizing burr size in drilling of AISI 316L stainless steel

This paper illustrates the methodology of genetic algorithm (GA) based multi-objective drilling process optimization. The optimal values of cutting speed, feed, point angle and lip clearance angle for a specified drill diameter were determined using GA, which simultaneously minimize burr height and burr thickness at the exit of holes during drilling of AISI 316L stainless steel. The burr size models required for GA optimization were developed using response surface methodology (RSM) with drilling experiments planned as per Box-Behnken design. The GA optimization results reveal that point angle has a significant role in controlling the burr size.     

A method of genetic algorithm based multiobjective optimization via cooperative coevolution

The paper deals with the identification of Pareto optimal solutions using GA based coevolution in the context of multiobjective optimization. Coevolution is a genetic process by which several species work with different types of individuals in parallel. The concept of cooperative coevolution is adopted to compensate for each of single objective optimal solutions during genetic evolution. The present study explores the GA based coevolution, and develops prescribed and adaptive scheduling schemes to reflect design characteristics among single objective optimization. In the paper, non-dominated Pareto optimal solutions are obtained by controlling scheduling schemes and comparing each of single objective optimal solutions. The proposed strategies are subsequently applied to a three-bar planar truss design and an energy preserving flywheel design to support proposed strategies.     

Design optimization of slender elastic beam with initial twist using genetic algorithms and finite element analysis

This paper aims to obtain the optimal composite slender beam design for a rotor blade. The cross-sectional dimensions are considered as design variables. The objective is to optimize the box beam to attain a target matrix of stiffness values and maximum elastic coupling. The optimization process is performed using a genetic algorithm (GA), associated with a variational asymptotic method for the structural analysis. The stiffness control of arbitrary, complex section under some design condition is performed in composite beam where the geometrically nonlinear characteristic of the structure is considered. The objective function is defined as the weight, strength and fatigue life. The laminate thicknesses are to be determined optimally by defining a design index comprising a weighted average of the objective functions and determining the minimum.     

基于遗传算法的圆柱齿轮变位系数的优化选择方法

合理地选择和分配齿轮传动机构中两齿轮的变位系数 ,能较大幅度地提高其齿面接触疲劳强度与齿根弯曲疲劳强度 ,减轻齿面磨损及防止胶合 ,从而设计出承载能力大、效率高、体积小、重量轻的齿轮机构。鉴于遗传算法的特点 ,提出了基于 GA的圆柱齿轮变位系数的优化方法 ,优化计算实例说明了该方法的可行性     

螺钉联接的可靠性优化设计

在鱼雷大段联接中,螺钉联接是一种广泛采用的方法.鱼雷壳体结构设计和强度分析过去一直采用以最大载荷、最小强度给出的安全系数来保证结构的可靠性,这里则提出使用可靠性理论和优化设计方法对螺钉联接进行再设计,并建立了相应的数学模型,利用遗传算法进行优化计算,经过计算取得了很好的成果.     

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.     

离散变量优化设计的改进斐波那契遗传算法

根据工程实际，充分考虑规范规定的约束条件和各项技术标准要求，建立离散变量结构优化模型。针对遗传算法在迭代过程中经常出现未成熟收敛、振荡、随机性太大和迭代过程缓慢等缺点，提出一种新的遗传算子——转基因算子，用于对遗传算法的改进；提出一种离散变量结构优化设计的斐波那契算法，并与遗传算法结合在一起解决问题。优化设计结果表明，这种改进斐波那契遗传算法的收敛特性得到很好的改善，即发挥了斐波那契算法省时、局部搜索能力强的特点，又发挥了遗传算法全局性好的特点，是有效的工程结构优化设计方法。     

柔性剪切蒙皮支撑结构的拓扑优化

为了求解关于柔性剪切蒙皮支撑结构的多目标拓扑优化问题,提出了一种带有多重约束处理能力的位矩阵表示的非支配排序遗传算法。采用位矩阵作为遗传算法的染色体并引入基于矩阵操作的遗传算子,利用Ansys有限元分析获得结构质量、面内剪切性能和面外承载能力等目标。利用Matlab处理结构连通性和面内应变等约束并实现了基于矩阵的优化算法,获得了一系列可行的柔性剪切蒙皮支撑结构,在实际应用中可以根据需要选择合适的结构。从研究结果可以看出,该算法可以给多目标二维结构拓扑优化问题提供可行有效的解。     

基于遗传算法的深沟球轴承优化设计研究

给出了基于遗传算法的深沟球轴承优化设计的方法。针对传统算法在深沟球轴承优化设计中全局寻优能力较差问题,以标准遗传算法（Genetic AlgorithmGA）的算法参数,采用了最优解保存策略,并运用罚函数法处理约束。为了降低摩擦阻力和减轻磨损,减少发热量,针对轴承发热变形的一个主要因素摩擦进行建模。     

A hybrid genetic algorithm for integrated process planning and scheduling problem with precedence constraints

Process planning and scheduling are two important functions in a modern manufacturing system. Although integrating decisions related to these functions gives rise to a hard combinatorial problem, due to the impressive improvement in system performance which is resulted through this integration, developing effective methods to solve this problem is of great theoretical and practical importance. In this research, after formulating the integrated process planning and scheduling problem as a mathematical program, we propose a hybrid genetic algorithm (GA) for the problem. In the proposed algorithm, problem-specific genetic operators are designed to enhance the global search power of GA. Also, a local search procedure has been incorporated into the GA to improve the performance of the algorithm. The model considers precedence relations among job operations, based on which feasible process plans for each job can be represented implicitly. A novel neighborhood function, considering the constraints of a flexible job shop environment and nonlinear precedence relations among operations, is presented to speed up the local search process. In experimental study, the performance of the proposed algorithm has been evaluated based on a number of problems adopted from the literature. The experimental results demonstrate the efficiency of the proposed algorithm to find optimal or near-optimal solutions.     

A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem

This paper presents a novel hybrid genetic algorithm (GA)-particle swarm optimization (PSO) approach for reliability redundancy allocation problem (RRAP) in series, series–parallel, and complex (bridge) systems. The proposed approach maximizes overall system reliability while minimizing system cost, system weight and volume, simultaneously, under nonlinear constraints. To meet these objectives, an adaptive hybrid GA–PSO approach is developed to identify the optimal solutions and improve computation efficiency for these NP-hard problems. An illustrative example is applied to show the capability and effectiveness of the proposed approach. According to the results, in all three cases, reliability values are improved. Moreover, computational time and variance are decreased compared to the similar studies. The proposed approach could be helpful for engineers and managers to better understand their system reliability and performance, and also to reach a better configuration.     

Estimation of Optimum Genetic Control Parameters for Job Shop Scheduling

Genetic algorithms (GA) have demonstrated considerable success in providing good solutions to many non-polynomial hard optimization problems. GAs are applied for identifying efficient solutions for a set of numerical optimization problems. Job shop scheduling (JSS) has earned a reputation for being difficult to solve. Many workers have used various values of genetic parameters. This paper attempts to tune the control parameters for efficiency, that are used to acceleate the genetic algorithm (applied to JSS) to converge on an optimal solution. The genetic parameters, namely, number of generations, probability of crossover, probability of mutation, are optimized relating to the size of problems. The results are validated in job shop scheduling problems. The results indicate that by using an appropriate range of parameters, the genetic algorithm is able to find an optimal solution faster. RID=" ID=" <E5>Correspondence and offprint requests to</E5>: Dr S. G. Ponnambalam, Department of Production Engineering, Regional Engineering College, Tiruchirapalli, 620 015, India. E-mail: pons&commat;rect.ernet.in     

基于精英选择自适应变异遗传算法的膜系优化设计

将改进的遗传算法——精英选择自适应变异遗传算法(EGAAM)用于光学薄膜的膜系优化设计。EGAAM采用了独特的自适应变异操作,该操作既使得群体保持了多样性,防止过早收敛,又加快了群体收敛速度。EGGAM对初始条件不敏感,并且可以确定膜层厚度边界,确保了设计结果的制备方便。通过减反膜、分光膜的实例优化设计表明,在相同设计条件下,用EGAAM可以得到比传统遗传算法更好的设计结果。理论与实例表明EGAAM用于膜系优化设计是高效和可靠的。     

机械手旋转移送机构的爆发式免疫优化

针对生产机械手旋转移送机构的优化设计问题,在免疫遗传算法(IGA)的基础上,先通过爆发式进化方式产生优势种群,然后与原种群进行杂交、基因比对等操作,从而提出了一种新型爆发式免疫进化算法(EIGA)。仿真实验结果表明,与遗传算法(GA)、IGA相比,EIGA具有收敛速度快、优化精度高和稳定性好的优点,能更好地实现机械手旋转移送机构的有效优化。     

Optimization of vendor managed inventory of multiproduct EPQ model with multiple constraints using genetic algorithm

The aim of this paper is to investigate the vendor managed inventory (VMI) problem of a single-vendor single-buyer supply chain system, in which the vendor is responsible to manage the buyer’s inventory. To include an extended applicability in real-world environments, the multiproduct economic production quantity model with backordering under three constraints of storage capacity, number of orders, and available budget is considered. The nonlinear programming model of the problem is first developed to determine the near optimal order quantities along with the maximum backorder levels of the products in a cycle such that the total VMI inventory cost of the system is minimized. Then, a genetic algorithm (GA) based heuristic is proposed to solve the model. Numerical examples are given to both demonstrate the applicability of the proposed methodology and to fine tune the GA parameters. At the end, the performance of the proposed GA is compared to the one of the LINGO software using different problem sizes. The results of the comparison study show that, while the solutions do not differ significantly, the proposed GA reaches near optimum solutions in significantly less amount of CPU time.     

Multiobjective optimization for force and moment balance of a four-bar linkage using evolutionary algorithms

In this study, force and moment balance of a planar four-bar linkage is implemented using evolutionary algorithms. In the current problem, the concepts of inertia counterweights and physical pendulum are utilized to complete balance of all mass effects, independent of input angular velocity. A proposed multiobjective particle swarm optimization, and non-dominated sorting genetic algorithm II are applied to minimize two objective functions subject to some design constraints. The applied algorithms produced a set of feasible solutions called pareto optimal solutions for the design problem. Finally, a fuzzy decision maker is utilized to select the best solution among the obtained pareto solutions. The results show that optimal solutions minimize the weights of applied counterweights and eliminate both shaking forces and moments transmitted to the ground, simultaneously.     

Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm

In this paper, parameter optimization of FSW of cryorolled AA2219 alloy was carried out to obtain defect free weld joint with maximum weld strength. To achieve this, artificial neural network (ANN) was used to model the relationship between the input parameters and the mechanical and corrosion properties (output) of the weld joints. The optimal FSW parameters were determined by genetic algorithm (GA). The feasible solution of the GA was tool rotational speed of 1005 rpm, tool travel speed of 20 mm/min and tool tilt angle of 3°. The feasible parameter was used to weld and check the ability of the parameter to produce better weld joint than the L₉ orthogonal array parameters. The weld, subjected to the confirmation test, was investigated by means of metallurgical, mechanical, and corrosion testing. This process reduces the costs associated with trial runs to obtain optimal parameters and also the production cost of the cryorolled (CR) plate which is high.