Runner sizing and weld line positioning for plastics injection moulding with multiple gates

Weld line and warping are two critical defects for injection-moulded part. If weld lines are unavoidable due to multiple gates in a moulding, they should be positioned in the non-critical region. Instead of using flow leaders and deflectors to control the locations of the weld lines, this paper presents an approach to specify the locations of the weld lines by sizing the runners in a multi-gated injection moulding with the optimum gate locations. The methodology consists of three steps: partitioning the moulding into sub-mouldings based on the specified weld line location, determining the optimum gate location in each sub-moulding by minimizing the injection pressure and repositioning the weld lines to the desired locations by varying the runner sizes. Two examples are used to illustrate the approach.     

一种改进的非支配排序多目标遗传算法

多目标进化算法的研究目标主要是使算法快速收敛,并且广泛而均匀分布于问题的非劣最优域。在NSGA-II算法的基础上,提出了一种新的构造种群的策略——按照聚集距离选取部分非支配个体,并选取部分较好的支配个体形成下一代种群。该策略与原算法相结合后的算法（NSGA-II+IMP）与原NSGA-II进行比较,结果表明新算法较好地改善了分布性和收敛性。     

基于非支配排序遗传算法的多学科鲁棒协同优化方法

针对鲁棒协同优化(robust collaborative optimization,RCO)具有两级优化结构和多目标形式的特点,提出基于非支配排序遗传算法(non-dominated sorting geneuc algorithm,NSGA-Ⅱ)的RCO求解方法.在NSGA-Ⅱ非支配排序中,根据个体的不可行度和不可...     

面向交通信号优化改进快速非支配排序遗传算法研究

交叉口作为交通流调度的重要组成部分,其交通信号配时将直接影响道路通行效率。针对快速非支配排序遗传算法(NSGA II)的精英保留策略会使大量冗余的高排序级别个体同时作为精英保留到下一代,极易发生早熟收敛现象问题,提出了改进的快速非支配排序遗传算法(I-NSGA II),并将其应用于交通信号多目标优化问题。I-NSGA II提出了冗余个体标记方法,之后的精英保留策略会通过该标记来判断去除冗余个体并将其并入临时层级,最后在生成的新种群规模不足时,会从临时层级中取出相应规模的冗余个体,对其进行变异操作后并入新种群。实验表明I-NSGA II在保证停车率和排队长度基本不变的情况下,减少了车辆及行人延误,证明所提出的算法可提高交通路口综合交通效益。     

A feature-based approach to injection mould cooling system design

Most existing work on the design of cooling systems of plastic injection moulds has been focused on the detailed analysis or the optimization of the cooling system. However, before a cooling system can be analysed or optimized, an initial design has to be developed. We explore a new design synthesis approach to solve this initial design problem. A plastic part with a complex shape is decomposed into simpler shape features. The cooling systems of the individual features are first obtained, they are then combined to form the cooling system of the entire part. Decomposing a complex shape into shape features is a feature recognition problem. A new algorithm for the recognition of features specific to cooling system design is developed. Design examples generated by the design synthesis process are analysed by C-Mold to verify the feasibility of the approach.     

基于精英选择和个体迁移的多目标遗传算法

提出基于遗传算法求解多目标优化问题的方法,将多目标问题分解成多个单目标优化问题,用遗传算法分别在每个单目标种群中并行搜索.在进化过程中的每一代,采用精英选择和个体迁移策略加快多个目标的并行搜索,提出了控制Pareto最优解数量并保持个体多样性的有限精度法,同时还提出了多目标遗传算法的终止条件.数值实验说明所提出的算法能较快地找到一组分布广泛且均匀的Pareto最优解.     

Cam shape optimisation by genetic algorithm

This article overviews a genetic algorithm based computer-aided approach for preliminary design and shape optimisation of cam profiles for cam operated mechanisms. The primary objective of the work was to create a complete systematic approach for preliminary cam shape design including cam shape design automation and true cam shape optimisation with respect to the simulated computer models of cam mechanisms. Typically, shape optimisation of a cam cross-section is a multiobjective optimisation problem of two-dimensional geometric shape in a heavily constrained environment. In order to illustrate the genetic algorithm based cam shape optimisation approach, a cam shape design example is described, in which a cam shape designed by genetic algorithm is compared with its more conventionally designed counterpart.     

Real-time task scheduling by multiobjective genetic algorithm

Real-time tasks are characterized by computational activities with timing constraints and classified into two categories: a hard real-time task and a soft real-time task. In hard real-time tasks, tardiness can be catastrophic. The goal of hard real-time tasks scheduling algorithms is to meet all tasks’ deadlines, in other words, to keep the feasibility of scheduling through admission control. However, in the case of soft real-time tasks, slight violation of deadlines is not so critical.In this paper, we propose a new scheduling algorithm for soft real-time tasks using multiobjective genetic algorithm (moGA) on multiprocessors system. It is assumed that tasks have precedence relations among them and are executed on homogeneous multiprocessor environment.The objective of the proposed scheduling algorithm is to minimize the total tardiness and total number of processors used. For these objectives, this paper combines adaptive weight approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. The effectiveness of the proposed algorithm is shown through simulation studies.     

基于新型非支配排序的多目标麻雀优化算法

针对麻雀搜索算法在求解多目标问题中的不足,并且在求解过程中易陷入局部最优与收敛性差的问题,提出了一种改进的多目标麻雀搜索算法。首先,引入了新型非支配排序,找到最优前沿面;其次,将多项式变异和正余弦算法融合到种群进化策略中,增强其搜索能力,通过竞争机制的种群选择方法,降低搜索过程中局部最优粒子和全局最优粒子导致的误差;最后,将改进算法与多种多目标算法在标准测试函数上进行对比,仿真结果表明,改进算法的收敛性与搜索能力均优于其他算法。由此说明该算法具有可靠的多目标寻优能力,能够有效解决多目标优化问题。     

Diversity of Pareto front: A multiobjective genetic algorithm based on dominating information

In this paper, the diversity information included by dominating number is analyzed, and the probabilistic relationship between dominating number and diversity in the space of objective function is proved. A ranking method based on dominating number is proposed to build the Pareto front. Without increasing basic Pareto method’s computation complexity and introducing new parameters, a new multiobjective genetic algorithm based on proposed ranking method (MOGA-DN) is presented. Simulation results on function optimization and parameters optimization of control system verify the efficiency of MOGA-DN.     

A genetic algorithm for multiple sequence alignment

Multiple sequence alignment is an important tool in molecular sequence analysis. This paper presents genetic algorithms to solve multiple sequence alignments. Several data sets are tested and the experimental results are compared with other methods. We find our approach could obtain good performance in the data sets with high similarity and long sequences.The software can be found in http://rsdb.csie.ncu.edu.tw/tools/msa.htm.     

A genetic algorithm for combined topology and shape optimisations

A method to find optimal topology and shape of structures is presented. With the first the optimal distribution of an assigned mass is found using an approach based on homogenisation theory, that seeks in which elements of a meshed domain it is present mass; with the second the discontinuous boundaries are smoothed. The problem of the optimal topology search has an ON/OFF nature and has suggested the employment of genetic algorithms. Thus in this paper a genetic algorithm has been developed, which uses as design variables, in the topology optimisation, the relative densities (with respect to effective material density) 0 or 1 of each element of the structure and, in the shape one, the coordinates of the keypoints of changeable boundaries constituted by curves. In both the steps the aim is that to find the variable sets producing the maximum stiffness of the structure, respecting an upper limit on the employed mass. The structural evaluations are carried out with a FEM commercial code, linked to the algorithm. Some applications have been performed and results compared with solutions reported in literature.     

改进NSGA-Ⅱ算法在锅炉燃烧多目标优化中的应用

提出改进非劣分类遗传算法(NSGA-Ⅱ)在燃煤锅炉多目标燃烧优化中的应用,优化的目标是锅炉热损失及NOx排放最小化。首先,采用BP神经网络模型分别建立了300MW燃煤锅炉的NOx排放特性模型和锅炉热损失模型,同时利用锅炉热态实验数据对模型进行了训练和验证,结果表明,BP神经网络模型可以很好地预测锅炉的排放特性和锅炉的热损失特性。在建立的锅炉排放特性和热损失BP神经网络模型基础上,采用非劣分类遗传算法对锅炉进行多目标优化,针对NSGA-Ⅱ在燃煤锅炉燃烧多目标优化问题应用中Pareto解集分布不理想、易早熟收敛的问题,在拥挤算子及交叉算子上进行了相应改进。优化结果表明,改进NSGA-Ⅱ方法与BP神经网络模型结合可以对锅炉燃烧实现有效的多目标寻优、得到理想的Pareto解,是对锅炉燃烧进行多目标优化的有效工具,同改进前的NSGA-Ⅱ优化结果比较,其Pareto优化结果集分布更好、解的质量更优。     

基于遗传算法的结晶器液位约束广义预测控制

针对实际连铸过程中结晶器液位控制须满足多种约束的问题,将一种基于遗传算法的有约束广义预测控制方法（ＧＣＧＰＣ）应用于结晶器液位控制．首先基于机理模型构造了有约束结晶器液位广义预测控制器;然后以遗传算法处理带约束的非线性优化问题,并以此作为滚动优化策略,求得最优控制律．仿真结果表明,在有效处理了约束的基础上,基于遗传算法的有约束广义预测控制效果优于ＰＩＤ 控制以及无约束广义预测控制．     

Non-dominated sorting genetic algorithm using fuzzy membership chromosome for categorical data clustering

In this research, a data clustering algorithm named as non-dominated sorting genetic algorithm-fuzzy membership chromosome (NSGA-FMC) based on K-modes method which combines fuzzy genetic algorithm and multi-objective optimization was proposed to improve the clustering quality on categorical data. The proposed method uses fuzzy membership value as chromosome. In addition, due to this innovative chromosome setting, a more efficient solution selection technique which selects a solution from non-dominated Pareto front based on the largest fuzzy membership is integrated in the proposed algorithm. The multiple objective functions: fuzzy compactness within a cluster (π) and separation among clusters (sep) are used to optimize the clustering quality. A series of experiments by using three UCI categorical datasets were conducted to compare the clustering results of the proposed NSGA-FMC with two existing methods: genetic algorithm fuzzy K-modes (GA-FKM) and multi-objective genetic algorithm-based fuzzy clustering of categorical attributes (MOGA (π, sep)). Adjusted Rand index (ARI), π, sep, and computation time were used as performance indexes for comparison. The experimental result showed that the proposed method can obtain better clustering quality in terms of ARI, π, and sep simultaneously with shorter computation time.     

Neuro-fuzzy and genetic algorithm in multiple response optimization

Optimization of a multiple output system, whose function is only approximately known and is represented in tabular form, is modeled and optimized by the combined use of a neuro-fuzzy network and optimization techniques which do not require the explicit representation of the function. Neuro-fuzzy network is useful for learning the approximate original tabular system. However, the results obtained by the neuro-fuzzy network are represented implicitly in the network. The MANFIS neuro-fuzzy network, which is an extension of the ANFIS network, is used to model the multiple output system and a genetic algorithm is used to optimize the resulting multiple objective decision making problem. A chemical process whose function is represented approximately in tabular form is solved to illustrate the approach.     

改进非支配排序进化算法在下料问题中的应用

针对一维下料问题,提出了减少废料、减少下料设置时间和减少可回收余料的三目标优化模型,用改进的非支配排序进化算法求出问题的Pareto最优解集,运用逼近理想解方法从解集中选出一个满意解作为下料方案,各优化目标的权重用CRITIC法算出。仿真实验证明了所提出的方法可以有效解决该类多目标下料问题。     

Evolution of mathematical models of chaotic systems based on multiobjective genetic programming

This work is concerned with the identification of models for nonlinear dynamical systems using multiobjective evolutionary algorithms. Systems modelling involves the processes of structure selection, parameter estimation, model performance and model validation and involves a complex solution space. Evolutionary Algorithms (EAs) are search and optimisation tools founded on the principles of natural evolution and genetics, which are suitable for a wide range of application areas. Due to the versatility of these tools and motivated by the versatility of genetic programming (GP), this evolutionary paradigm is proposed for this modelling problem. GP is then combined with a multiobjective function definition scheme. Multiobjective genetic programming (MOGP) is applied to multiple, conflicting objectives and yields a set of candidate parsimonious and valid models, which reproduce the original system behaviour. The MOGP approach is then demonstrated as being applicable for system modelling with chaotic dynamics. The circuit introduced by Chua, being one of the most popular benchmarks for studying nonlinear oscillations, and the Duffing–Holmes oscillator are the systems to test the evolutionary-based modelling approach introduced in this paper.     

A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment

This paper deals with a problem of reconfigurable manufacturing systems (RMSs) design based on products specifications and reconfigurable machines capabilities. A reconfigurable manufacturing environment includes machines, tools, system layout, etc. Moreover, the machine can be reconfigured to meet the changing needs in terms of capacity and functionality, which means that the same machine can be modified in order to perform different tasks depending on the offered axes of motion in each configuration and the availability of tools. This problem is related to the selection of candidate reconfigurable machines among an available set, which will be then used to carry out a certain product based on the product characteristics. The selection of the machines considers two main objectives respectively the minimization of the total cost (production cost, reconfiguration cost, tool changing cost and tool using cost) and the total completion time. An adapted version of the non- dominated sorting genetic algorithm (NSGA-II) is proposed to solve the problem. To demonstrate the effectiveness of the proposed approach on RMS design problem, a numerical example is presented and the obtained results are discussed with suggested future research.     

A GA/gradient hybrid approach for injection moulding conditions optimisation

Injection moulding conditions such as melt temperature, mould temperature and injection time are important process parameters. Optimisation of these parameters involve complex patterns of local minima, which makes it very suited for Genetic Algorithm (GA). However, once a minimal region is identified during the search process, the GA method is not efficient, even sometimes impossible, in reaching its minimum. This is because GA is opportunistic not deterministic. The crossover and mutation operation may lead the search out of the identified minimal region. Gradient methods, on the other hand, are very efficient in this regard and can guarantee a local minimum, but not a global one. In this paper, a strategy of using a hybrid of both methods in injection moulding conditions optimisation is proposed, so as to exploit their respective advantages. The hybrid optimisation process is elaborated and a case study is conducted to test the effectiveness and efficiency of the strategy and its implementation algorithm. The optimisation results from the hybrid approach are compared with those from the GA method alone to demonstrate the improvement.