Ply stacking sequence optimization of composite laminate by permutation discrete particle swarm optimization

Stacking sequence optimization (SSO) of laminate will greatly improve its mechanical properties without weight penalty. In this paper, a novel permutation discrete particle swarm optimization (PDPSO) method was proposed to perform SSO. To improve the efficiency of the algorithm, the concepts and techniques of valid/invalid exchange, checking memory and Self-escape were introduced into the PDPSO. In total 11 examples were presented. First, eight examples were carried out by employing the proposed method. The results show that the computational efficiency of PDPSO is greatly improved compared with standard discrete particle swarm optimization (SDPSO), and is comparable with that of gene rank crossover (GR) and partially mapped crossover (PMX). Then, three extra examples were presented, in which the outermost plies in the optimum design are not ±45° plies. The results show that the PDPSO has better stability and potential which demonstrate the better performance of PDPSO for laminates.     

Optimum stacking sequence design of composite laminates for maximum buckling load capacity using parameter-less optimization algorithms

This paper presents a comparative study of the application of parameter-less meta-heuristic algorithms in optimum stacking sequence design of com of composite laminates for maximum buckling load capacity. Here, JAYA algorithm, along with Salp Swarm Algorithm, Colliding Bodies Optimization, Grey Wolf Optimizer, and Genetic Algorithm with standard setting and self-adaptive version are implemented to the problem of composite laminates with 64 graphite/epoxy plies with conventional ply angles, under several bi-axial cases and panel aspect ratios. Optimization objective is to maximize the buckling load of symmetric and balanced laminated plate. Statistical analysis are performed for six cases and the results are compared in terms of mean, standard deviation, the coefficient of variation, best and worst solutions, accompanied by the percentage of the independent runs that found the global optimum \(\left( {{R_{{\text{op}}}}} \right)\) and near global optimum \(\left( {{R_{{\text{no}}}}} \right)\). The Kruskal–Wallis nonparametric test is also utilized to make further confidence in the examinations. Numerical results show the high capability of the JAYA algorithm for maximizing the buckling capacity of composite plates.

     

Matrix-free aerostructural optimization of aircraft wings

In structural optimization subject to failure constraints, computing the gradients of a large number of functions with respect to a large number of design variables may not be computationally practical. Often, the number of constraints in these optimization problems is reduced using constraint aggregation at the expense of a higher mass of the optimal structural design. This work presents results of structural and coupled aerodynamic and structural design optimization of aircraft wings using a novel matrix-free augmented Lagrangian optimizer. By using a matrix-free optimizer, the computation of the full constraint Jacobian at each iteration is replaced by the computation of a small number of Jacobian-vector products. The low cost of the Jacobian-vector products allows optimization problems with thousands of failure constraints to be solved directly, mitigating the effects of constraint aggregation. The results indicate that the matrix-free optimizer reduces the computational work of solving the optimization problem by an order of magnitude compared to a traditional sequential quadratic programming optimizer. Furthermore, the use of a matrix-free optimizer makes the solution of large multidisciplinary design problems, in which gradient information must be obtained through iterative methods, computationally tractable.     

Interval-based optimization of aircraft wings under landing loads

An interval-based automated optimization of aircraft wing structures subjected to landing loads is discussed in this paper. The interaction between landing gear and flexible airplane structure is considered as a coupled system. The uncertain system parameters are described as interval numbers. The computational aspects of the optimization procedure are illustrated with two examples – symmetric double-wedge airfoil, and supersonic airplane wing. Since, in most cases only the ranges of uncertain parameters are known with their probability distribution functions unknown, the present methodology is expected to be more realistic for the optimum design of aircraft structures under landing loads.     

Modified element stacking method for multi-material topology optimization with anisotropic materials

Structural and Multidisciplinary Optimization - This article presents a modified element stacking method for anisotropic multi-material topology optimization. This method can transform standard...     

Constrained-manufacturable stacking sequence design optimization using an improved global shared-layer blending method and its 98-line Matlab code

An improved global shared-layer blending method (GSLB) is suggested to address the constrained-manufacturable stacking sequence design optimization problem of tapered composite structures. First, the mathematical model for tapered composite structures design problem is constructed and the effect of blending constraint on the design space is analyzed. By introducing the set theory, the original GSLB method is improved by aggregating a shape prediction algorithm and a thickness evaluation procedure. The shape prediction algorithm takes advantage of the set computation procedure, which simplifies the process for detecting the shared layers’ boundaries. The maximum blending shared layers are evaluated by the improved GSLB in terms of the thickness distribution of multiple ply orientations. Subsequently, the obtained shared-layers are served as integrated variables for stacking sequence design, in which complex manufacturing constraints are involved. Three multi-panel structures and a wing box structure are adopted to verify the improved GSLB method and stacking sequence design strategy, and perfectly blended solutions are found without violation of manufacturing constraints and mechanical requirements. Finally, the 98 line Matlab code of the improved GSLB method is provided for the convenience of engineering application. This research has two purposes: providing a technique for tailoring design of tapered composite structures and giving reference solutions for constrained-manufacturable stacking sequence design optimization problem.

     

自动化集装箱码头穿越式双自动堆码起重机调度优化

针对穿越式双自动堆码起重机(ASC)调度优化问题,考虑执行同一贝位任务时ASC间发生冲突的可能性,建立多目标混合整数规划模型。对双ASC执行任务的序列进行优化,得出规避冲突后的最优作业序列、作业完成时间和空载时间成本,证明了优化模型的实用性。为进一步比较穿越式双ASC与单ASC设备的效率差异,及分析参数对结果的影响,设计四组实验场景。分析结果表明:双ASC作业下的设备利用率比单ASC低107%,而作业效率比单ASC高35%;集装箱任务数的减少和ASC载具移动速度的增大会降低ASC的作业时间成本;存取比例为1时将得到最少的完成时间和空载时间。由结果可知,码头可通过调整ASC在一段时间内作业的存取箱比例,或不断优化ASC载具移动速度,优化整个任务的作业效率和设备利用率。     

Derivative based approximation for predicting the effect of changes in laminate stacking sequence

Derivatives of buckling loads of stiffened panels with respect to ply thicknesses are easy to calculate. Consequently, such derivatives are often available in computer programs that calculate buckling loads of composite structures. These derivatives can be used to construct approximations of the dependence of the buckling load on ply thicknesses. The present work demonstrates the use of derivatives of buckling loads with respect to ply thicknesses to approximate the effects of changes in stacking sequence and ply orientations on buckling load of a laminate. Examples of unstiffened and stiffened panels are used to demonstrate the effectiveness of the proposed approximation.     

Composite laminate optimization program suitable for microcomputers

The design of a composite panel requires some way of finding the minimum thickness laminate which will withstand the load requirements without failure. The mathematical complexity of this problem dictates the use of nonlinear optimization techniques. Although there are sophisticated optimization programs available capable of solving for the ply ratios, these programs are not often used in preliminary design because they require a large computer and some knowledge of the program's operation. As an alternative, specialized laminate optimization programs were developed which are compact and efficient enough to run on microcomputers. Only stresses at a point and inplane loads and deflections are considered. The programs are simple to use and require no knowledge of optimization. Techniques are developed in this paper that find minimum thickness laminates with either ply ratios or ply angles as design variables. Many test cases were run with these programs to demonstrate the weight savings possible over quasi-isotropic laminates. Particular interest is directed toward performance of the laminates under multiple independent loads. Initial orientations for the programs to operate on were studied, and 0/90/45/-45 laminates were found to be an effective starting point for design.     

Shape optimization of underwater wings with a new multi-fidelity bi-level strategy

Structural and Multidisciplinary Optimization - This paper proposes a new multi-fidelity bi-level optimization (MFBLO) strategy for shape designs of underwater wings. Firstly, hydrodynamic analyses...     

A modified ant colony algorithm for the stacking sequence optimisation of a rectangular laminate

This paper presents a modified Ant Colony Algorithm (ACA) called multi-city-layer ant colony algorithm (MCLACA). The research attention is focused on improving the computational efficiency in the stacking sequence optimisation of a laminated composite plate for maximum buckling load. A new operator, the so-called two point interchange, is introduced and proved to be effective for reducing the convergence time and enhancing the robustness in the MCLACA performance. The laminate optimisation is subject to balanced and symmetric layup with ply contiguous and strength constraints. In order to assess the MCLACA performance, a simply supported rectangular laminate plate, which was taken as numerical example in previous research using traditional ACA and genetic algorithm (GA) is chosen as a benchmark case study. Comparing with the ACA and GA results, it is shown that the presented MCLACA has better performance in terms of computational efficiency and robustness. To demonstrate the applicability of the MCLACA to a general case, an additional example of laminate optimisation has been taken with more design variables and five different boundary conditions by finite element analysis.     

Chaotic particle swarm optimization for assembly sequence planning

Assembly sequence planning of complex products is difficult to be tackled, because the size of the search space of assembly sequences is exponentially proportional to the number of parts or components of the products. Contrasted with the conventional methods, the intelligent optimization algorithms display their predominance in escaping from the vexatious trap. This paper proposes a chaotic particle swarm optimization (CPSO) approach to generate the optimal or near-optimal assembly sequences of products. Six kinds of assembly process constraints affecting the assembly cost are concerned and clarified at first. Then, the optimization model of assembly sequences is presented. The mapping rules between the optimization model and the traditional PSO model are given. The variable velocity in the traditional PSO algorithm is changed to the velocity operator (vo) which is used to rearrange the parts in the assembly sequences to generate the optimal or near-optimal assembly sequences. To improve the quality of the optimal assembly sequence and increase the convergence rate of the traditional PSO algorithm, the chaos method is proposed to provide the preferable assembly sequences of each particle in the current optimization time step. Then, the preferable assembly sequences are considered as the seeds to generate the optimal or near-optimal assembly sequences utilizing the traditional PSO algorithm. The proposed method is validated with an illustrative example and the results are compared with those obtained using the traditional PSO algorithm under the same assembly process constraints.     

Stacking sequence optimization for constant stiffness laminates based on a continuous optimization approach

In this paper, an optimization procedure based on multi-phase topology optimization is developed to determine the optimal stacking sequence of laminates made up of conventional plies oriented at ?45°, 0°, 45 and 90°. The formulation relies on the SFP (Shape Functions with Penalization) parameterization, in which the discrete optimization problem is replaced by a continuous approach with a penalty to exclude intermediate values of the design variables. In this approach, the material stiffness of each physical ply is expressed as a weighted sum over the stiffness of the candidate plies corresponding to ?45°, 0°, 45 and 90° orientations. In SFP, two design variables are needed for each physical ply in the laminate to parameterize the problem with respect to the 4 candidate orientations. Even if only constant stiffness laminates of constant thickness are considered in this paper, specific design rules used in aeronautics for composite panels (i.e., no more than a maximum number of consecutive plies with the same orientation in the stacking sequence) are however formulated and taken into account in the optimization problem. The methodology is demonstrated on an application. It is discussed how the different design rules can affect the solution.     

Parametric optimization of sequence alignment

Theoptimal alignment or theweighted minimum edit distance between two DNA or amino acid sequences for a given set of weights is computed by classical dynamic programming techniques, and is widely used in molecular biology. However, in DNA and amino acid sequences there is considerable disagreement about how to weight matches, mismatches, insertions/deletions (indels or spaces), and gaps.Parametric sequence alignment is the problem of computing the optimal-valued alignment between two sequences as afunction of variable weights for matches, mismatches, spaces, and gaps. The goal is to partition the parameter space into regions (which are necessarily convex) such that in each region one alignment is optimal throughout and such that the regions are maximal for this property. In this paper we are primarily concerned with the structure of this convex decomposition, and secondarily with the complexity of computing the decomposition. The most striking results are the following: For the special case where only matches, mismatches, and spaces are counted, and where spaces are counted throughout the alignment, we show that the decomposition is surprisingly simple: all regions are infinite; there are at most n^2/3 regions; the lines that bound the regions are all of the form =c + (c + 0.5); and the entire decomposition can be found inO(knm) time, wherek is the actual number of regions, andn相似文献   

A hybrid cuckoo search-genetic algorithm for hole-making sequence optimization

Biologically-inspired algorithms are stochastic search methods that emulate the behavior of natural biological evolution to produce better solutions and have been widely used to solve engineering optimization problems. In this paper, a new hybrid algorithm is proposed based on the breeding behavior of cuckoos and evolutionary strategies of genetic algorithm by combining the advantages of genetic algorithm into the cuckoo search algorithm. The proposed hybrid cuckoo search-genetic algorithm (CSGA) is used for the optimization of hole-making operations in which a hole may require various tools to machine its final size. The main objective considered here is to minimize the total non-cutting time of the machining process, including the tool positioning time and the tool switching time. The performance of CSGA is verified through solving a set of benchmark problems taken from the literature. The amount of improvement obtained for different problem sizes are reported and compared with those by ant colony optimization, particle swarm optimization, immune based algorithm and cuckoo search algorithm. The results of the tests show that CSGA is superior to the compared algorithms.     

多目标优化机制下DNA编码序列模型

针对现有DNA计算中存在的编码序列设计稳定性、可靠性不完善等问题,充分考虑基本编码问题,设计出一种基于多目标优化机制的DNA编码序列设计算法。在一定的约束条件下,该算法利用了多目标优化机制以及采取小种蚁群算法,将h-distance因子添加到单链DNA架构中,建立一种DNA序列公用方法。通过模拟实验表明,该算法与同类型算法相比,在计算效率、优化性方面具有一定优势。     

基于T细胞调节的免疫遗传优化算法对逆变器PWM控制序列的优化

针对遗传算法(GAs)收敛速度慢、易于陷入局部最优等不足,基于单相全桥逆变器输出电流与参考电流的误差模型,提出一种改进的免疫遗传优化算法(IGOAs)用于逆变器PWM最优控制序列优化。算法采用0、1编码,自适应突变概率及T细胞调节算子增强算法的快速收敛性和种群的多样性。数值实验中考虑逆变器负载端电阻为定值和受随机扰动两种情形,将GAs和IGOAs用于此两种情形的PWM控制序列优化,仿真结果统计表明：IGOAs具有较好的收敛性和稳定性,负载电阻受随机扰动时,IGOAs较GAs能快速跟踪参考电流,获得较小的THD电流波。     

多模式系统的测试顺序优化

研究了多模式系统的测试顺序优化问题。基于不同模式下测试与故障之间的依赖关系,结合系统故障的先验概率、可用测试的成本以及不同模式的转换费用,构造了该问题的数学描述模型。基于已有的搜索算法提出了一种准多步前向搜索算法,该算法以信息增益为启发策略,可自动获取平均测试费用最少、且能快速实现系统故障检测与隔离的优化测试顺序。最后实例验证了该算法的正确性,证明该算法可解决实际问题。