Genetic algorithm attributes for component selection

This paper uses a genetic algorithm for component selection given a user-defined system layout, a database of components, and a defined set of design specifications. A genetic algorithm is a search method based on the principles of natural selection. An introduction to genetic algorithms is presented, and genetic algorithm attributes that are useful for component selection are explored. A comparison of these attributes is performed using two industrial design problems. A set of genetic algorithm attributes including integer coding, uniform crossover, anti-incest mating, variable mating and mutation rates, retention of population members from generation to generation, and an attention shifted penalty function are suggested for a more efficient search in component selection problems.     

基于进化算法的产品造型创新设计方法研究

为了满足用户多样化的产品造型需求,模拟设计师的设计思维特性,提出了应用元胞遗传算法和标准遗传算法的产品造型创新设计新方法.首先收集产品样本,经聚类分析、设计师聚焦等确定代表性产品样本,再利用形态分析法得到产品造型元素并定量描述设计参数;其次,以代表性产品样本为初始种群,应用元胞遗传算法建立产品造型初始设计系统,实现了以少量原型生成大量创新性方案的智能设计进程;最后,应用标准遗传算法建立产品造型细化设计系统,进一步优化初始设计方案,快速实现方案的细化智能设计进程.卡通表情造型设计实例表明,该方法可为创新设计提供有效的辅助与支持.     

A surrogate assisted parallel multiobjective evolutionary algorithm for robust engineering design

A number of multi-objective evolutionary algorithms have been proposed in recent years and many of them have been used to solve engineering design optimization problems. However, designs need to be robust for real-life implementation, i.e. performance should not degrade substantially under expected variations in the variable values or operating conditions. Solutions of constrained robust design optimization problems should not be too close to the constraint boundaries so that they remain feasible under expected variations. A robust design optimization problem is far more computationally expensive than a design optimization problem as neighbourhood assessments of every solution are required to compute the performance variance and to ensure neighbourhood feasibility. A framework for robust design optimization using a surrogate model for neighbourhood assessments is introduced in this article. The robust design optimization problem is modelled as a multi-objective optimization problem with the aim of simultaneously maximizing performance and minimizing performance variance. A modified constraint-handling scheme is implemented to deal with neighbourhood feasibility. A radial basis function (RBF) network is used as a surrogate model and the accuracy of this model is maintained via periodic retraining. In addition to using surrogates to reduce computational time, the algorithm has been implemented on multiple processors using a master–slave topology. The preliminary results of two constrained robust design optimization problems indicate that substantial savings in the actual number of function evaluations are possible while maintaining an acceptable level of solution quality.     

A robust cellular manufacturing system design for dynamic part population using a genetic algorithm

As changing conditions prevail in the manufacturing environment, the design of cellular manufacturing systems, which involves the formation of part families and machine cells, is difficult. This is due to the fact that the machines need to be relocated as per the requirements if adaptive designs are used. This paper presents a new approach (robust design) for forming part families and machine cells, which can handle all the changes in demands and product mixes without any relocations. This method suggests fixed machine cells for the dynamic nature of the production environment by considering a multi-period forecast of product mix and demand. A genetic algorithm based solution procedure is adopted to solve the problem. The results thus obtained were compared with the adaptive design proposed by Wicks and Reasor (1999 Wicks, EM and Reasor, RJ. 1999. Designing cellular manufacturing systems with dynamic part populations. IIE Trans., 31: 11–20. [Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). It is found that the robust design performs better than the adaptive design for the problems attempted.     

GGREADA: A graph grammar-based machine design algorithm

A class of graph grammar-based design algorithms for the generation of near-optimal machine designs is proposed. The GGREADA algorithm is implemented to design scale model carts from Meccano^® Set components. A graph grammar for the carts is defined. GGREADA successfully designs minimum-weight carts that provide specified load-bed areas. GGREADA's exploration capability is also demonstrated by creating probability frequency plots of likely cart surface areas and weights from random samples taken from the space of all possible cart designs. The advantages that a designer may gain from grammar-based design tools are discussed along with the representational challenges that remain in exploiting grammars for this purpose.     

Automated synthesis of modular fixture designs using an evolutionary search algorithm

Fixture design is a complex problem that requires a designer to ensure that a workpiece is located deterministically, totally restrained and sufficiently supported during a manufacturing process. The use of modular fixtures, while presenting an opportunity to improve the responsiveness of a manufacturing system, adds to the complexity of the fixture design problem. The complexity is a result of the large number of fixture elements in a modular fixture system and the constraints of specified locations in which fixture elements can be placed in a grid-based modular system. This paper presents an evolutionary search algorithm that aids a fixture designer by exploring the large number of possible fixture designs and suggesting an appropriate one. The algorithm can explore the large solution space using a flexible and generic representation and it considers fixture layout and fixture configuration constraints concurrently in arriving at appropriate solutions. The initial results of the algorithm are promising.     

An evolutionary algorithm to generate alternatives (EAGA) for engineering optimization problems

Typically for a real optimization problem, the optimal solution to a mathematical model of that real problem may not always be the ‘best’ solution when considering unmodeled or unquantified objectives during decision-making. Formal approaches to explore efficiently for good but maximally different alternative solutions have been established in the operations research literature, and have been shown to be valuable in identifying solutions that perform expectedly well with respect to modeled and unmodeled objectives. While the use of evolutionary algorithms (EAs) to solve real engineering optimization problems is becoming increasingly common, systematic alternatives-generation capabilities are not fully extended for EAs. This paper presents a new EA-based approach to generate alternatives (EAGA), and illustrates its applicability via two test problems. A realistic airline route network design problem was also solved and analyzed successfully using EAGA. The EAGA promises to be a flexible procedure for exploring alternative solutions that could assist when making decisions for real engineering optimization problems riddled with unmodeled or unquantified issues.     

Kansei knowledge extraction based on evolutionary genetic algorithm: an application to e-commerce web appearance design

Considering the overall consumer preference based on Kansei engineering, this paper focuses on optimising the appearance design of e-commerce web. In the beginning, we have used iView X RED Eye Tracking Systems experimental apparatus produced by SensoMotoric Instruments (SMI) in Germany to extract web design elements, and then several representative webs are designed based on orthogonal test design, following by surveys we have made. Furthermore, structural equation models are established in order to obtain a single preference factor on the influence of e-commerce web design. Finally, based on the neural networks (NNs) and evolutionary genetic algorithm approach, the global optimisation appearance design of the e-commerce web is fetched by simulation on computer, providing the effective suggestions for the e-commerce web designer. This research paper presents a systematic approach that convert consumer's Kansei knowledge into usable product multi-dimensional design variables.     

遗传算法在曲柄摇杆机构设计中的应用

本文讨论曲柄摇杆机构优化设计的数学模型及其简化 ,利用遗传算法得到优化结果     

A simulation-based multi-objective genetic algorithm (SMOGA) procedure for BOT network design problem

Solving optimization problems with multiple objectives under uncertainty is generally a very difficult task. Evolutionary algorithms, particularly genetic algorithms, have shown to be effective in solving this type of complex problems. In this paper, we develop a simulation-based multi-objective genetic algorithm (SMOGA) procedure to solve the build-operate-transfer (BOT) network design problem with multiple objectives under demand uncertainty. The SMOGA procedure integrates stochastic simulation, a traffic assignment algorithm, a distance-based method, and a genetic algorithm (GA) to solve a multi-objective BOT network design problem formulated as a stochastic bi-level mathematical program. To demonstrate the feasibility of SMOGA procedure, we solve two mean-variance models for determining the optimal toll and capacity in a BOT roadway project subject to demand uncertainty. Using the inter-city expressway in the Pearl River Delta Region of South China as a case study, numerical results show that the SMOGA procedure is robust in generating ‘good’ non-dominated solutions with respect to a number of parameters used in the GA, and performs better than the weighted-sum method in terms of the quality of non-dominated solutions.     

Robust motion control design for dual-axis motion platform using evolutionary algorithm

This paper presents a new approach to deal with the dual-axis control design problem for a mechatronic platform. The cross-coupling effect leading to contour errors is effectively resolved by incorporating a neural net-based decoupling compensator. Conditions for robust stability are derived to ensure the closed-loop system stability with the decoupling compensator. An evolutionary algorithm possessing the universal solution seeking capability is proposed for finding the optimal connecting weights of the neural compensator and PID control gains for the X and Y axis control loops. Numerical studies and a real-world experiment for a watch cambered surface polishing platform have verified performance and applicability of our proposed design.     

An orthogonal design based constrained evolutionary optimization algorithm

Solving constrained optimization problems (COPs) via evolutionary algorithms (EAs) has attracted much attention. In this article, an orthogonal design based constrained optimization evolutionary algorithm (ODCOEA) to tackle COPs is proposed. In principle, ODCOEA belongs to a class of steady state evolutionary algorithms. In the evolutionary process, several individuals are chosen from the population as parents and orthogonal design is applied to pairs of parents to produce a set of representative offspring. Then, after combining the offspring generated by different pairs of parents, non-dominated individuals are chosen. Subsequently, from the parent’s perspective, it is decided whether a non-dominated individual replaces a selected parent. Finally, ODCOEA incorporates an improved BGA mutation operator to facilitate the diversity of the population. The proposed ODCOEA is effectively applied to 12 benchmark test functions. The computational experiments show that ODCOEA not only quickly converges to optimal or near-optimal solutions, but also displays a very high performance compared with another two state-of-the-art techniques.     

A multicriteria evolutionary algorithm for mechanical design optimization with expert rules

This paper addresses the problem of optimizing mechanical components during the first stage of the design process. While a previous study focused on parameterized designs with fixed configurations—which led to the development of the PAMUC (Preferences Applied to Multiobjectivity and Constraints) method, to tackle constraints and preferences in evolutionary algorithms (EAs)—, the models to be considered in this work are enriched by the presence of topological variables. In this context, in order to create optimal but also realistic designs, i.e. fulfilling not only technical requirements but also technological constraints (more naturally expressed in terms of rules), a novel approach is proposed: PAMUC II. It consists in integrating an inference engine within the EA to repair the individuals violating the user‐defined rules. PAMUC II is tested on mechanical benchmarks, and provides very satisfactory results in comparison with a weighted sum method with penalization to deal with the constraints. Copyright © 2004 John Wiley & Sons, Ltd.     

A genetic algorithm with memory for optimal design of laminated sandwich composite panels

This paper is concerned with augmenting genetic algorithms (GAs) to include memory for continuous variables, and applying this to stacking sequence design of laminated sandwich composite panels that involves both discrete variables and a continuous design variable. The term “memory” implies preserving data from previously analyzed designs. A balanced binary tree with nodes corresponding to discrete designs renders efficient access to the memory. For those discrete designs that occur frequently, an evolving database of continuous variable values is used to construct a spline approximation to the fitness as a function of the single continuous variable. The approximation is then used to decide when to retrieve the fitness function value from the spline and when to do an exact analysis to add a new data point for the spline. With the spline approximation in place, it is also possible to use the best solution of the approximation as a local improvement during the optimization process. The demonstration problem chosen is the stacking sequence optimization of a sandwich plate with composite face sheets for weight minimization subject to strength and buckling constraints. Comparisons are made between the cases with and without the binary tree and spline interpolation added to a standard GA. Reduced computational cost and increased performance index of a GA with these changes are demonstrated.     

Optimal part orientation in layered manufacturing using evolutionary stickers-based DNA algorithm

Over recent years, layered manufacturing (LM) has been one of the most important emerging research areas, as well as practice perspective, owing to its capability to reduce the product development time, and therefore time-to-market. In LM, owing to the significant role played by the part orientation in the successful and efficient reduction of the staircase effect, the determination of optimal part orientation is a matter of paramount importance. In this research, the dual parameters problem has been modelled, taking into consideration the constraints pertaining to the rotation of the computer aided design (CAD) model about two axes, while aiming to optimize the objective function that involves layered process error as well as build time. The current paper presents an advanced stickers-based DNA algorithm (SDNA) inspired by the characteristics of deoxyribonucleic acid (DNA) as a tool to achieve the optimal orientation during fabrication of a part. The salient feature of the proposed algorithm is the use of stickers along with DNA memory strand, which are responsible for the representation of information. Moreover, fundamental operations are applied to manipulate the positions of the stickers in essentially all the possible ways. The performance of SDNA has been tested on two standard case studies and the comparisons are made with results obtained from genetic algorithm (GA). The results clearly demonstrate the efficacy of proposed algorithm over GA when applied to the underlying problems.     

Improved genetic algorithm for mixed-discrete-continuous design optimization problems

An improved genetic algorithm (IGA) is presented to solve the mixed-discrete-continuous design optimization problems. The IGA approach combines the traditional genetic algorithm with the experimental design method. The experimental design method is incorporated in the crossover operations to systematically select better genes to tailor the crossover operations in order to find the representative chromosomes to be the new potential offspring, so that the IGA approach possesses the merit of global exploration and obtains better solutions. The presented IGA approach is effectively applied to solve one structural and five mechanical engineering problems. The computational results show that the presented IGA approach can obtain better solutions than both the GA-based and the particle-swarm-optimizer-based methods reported recently.     

Multi-objective optimization of a low specific speed centrifugal pump using an evolutionary algorithm

This paper describes the shape optimization of a low specific speed centrifugal pump at the design point. The target pump has already been manually modified on the basis of empirical knowledge. A genetic algorithm (NSGA-II) with certain enhancements is adopted to improve its performance further with respect to two goals. In order to limit the number of design variables without losing geometric information, the impeller is parametrized using the Bézier curve and a B-spline. Numerical simulation based on a Reynolds averaged Navier–Stokes (RANS) turbulent model is done in parallel to evaluate the flow field. A back-propagating neural network is constructed as a surrogate for performance prediction to save computing time, while initial samples are selected according to an orthogonal array. Then global Pareto-optimal solutions are obtained and analysed. The results manifest that unexpected flow structures, such as the secondary flow on the meridian plane, have diminished or vanished in the optimized pump.     

Optimal topology design of industrial structures using an evolutionary algorithm

The paper demonstrates the application of a modified Evolutionary Structural Optimisation (ESO) algorithm for optimal design of topologies for complex structures. A new approach for adaptively controlling the material elimination and a ‘gauss point average stress’ is used as the ESO criterion in order to reduce the generation of checkerboard patterns in the resultant optimal topologies. Also, a convergence criterion is used to examine the uniformity of strength throughout a structure. The ESO algorithm is validated by comparing the ESO based solution with the result obtained using another numerical optimisation method (SIMP).     

An effective hybrid cuckoo search and genetic algorithm for constrained engineering design optimization

This article presents an effective hybrid cuckoo search and genetic algorithm (HCSGA) for solving engineering design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. The proposed algorithm, HCSGA, is first applied to 13 standard benchmark constrained optimization functions and subsequently used to solve three well-known design problems reported in the literature. The numerical results obtained by HCSGA show competitive performance with respect to recent algorithms for constrained design optimization problems.     

A new optimization algorithm for solving complex constrained design optimization problems

This article presents the performance of a very recently proposed Jaya algorithm on a class of constrained design optimization problems. The distinct feature of this algorithm is that it does not have any algorithm-specific control parameters and hence the burden of tuning the control parameters is minimized. The performance of the proposed Jaya algorithm is tested on 21 benchmark problems related to constrained design optimization. In addition to the 21 benchmark problems, the performance of the algorithm is investigated on four constrained mechanical design problems, i.e. robot gripper, multiple disc clutch brake, hydrostatic thrust bearing and rolling element bearing. The computational results reveal that the Jaya algorithm is superior to or competitive with other optimization algorithms for the problems considered.