A cooperative coevolutionary algorithm for design of platform-based mass customized products

As a new business model, mass customization (MC) intends to enable enterprises to comply with customer requirements at mass production efficiencies. A widely advocated approach to implement MC is platform product customization (PPC). In this approach, a product variant is derived from a given product platform to satisfy customer requirements. Adaptive PPC is such a PPC mode in which the given product platform has a modular architecture where customization is achieved by swapping standard modules and/or scaling modular components to formulate multiple product variants according to market segments and customer requirements. Adaptive PPC optimization includes structural configuration and parametric optimization. This paper presents a new method, namely, a cooperative coevolutionary algorithm (CCEA), to solve the two interrelated problems of structural configuration and parametric optimization in adaptive PPC. The performance of the proposed algorithm is compared with other methods through a set of computational experiments. The results show that CCEA outperforms the existing hierarchical evolutionary approaches, especially for large-scale problems tested in the experiments. From the experiments, it is also noticed that CCEA is slow to converge at the beginning of evolutionary process. This initial slow convergence property of the method improves its searching capability and ensures a high quality solution.     

An adaptive clustering-based genetic algorithm for the dual-gantry pick-and-place machine optimization

This research proposes an adaptive clustering-based genetic algorithm (ACGA) to optimize the pick-and-place operation of a dual-gantry component placement machine, which has two independent gantries that alternately place components onto a printed circuit board (PCB). The proposed optimization problem consists of several highly interrelated sub-problems, such as component allocation, nozzle and feeder setups, pick-and-place sequences, etc. In the proposed ACGA, the nozzle and component allocation decisions are made before the evolutionary search of a genetic algorithm to improve the algorithm efficiency. First, the nozzle allocation problem is modeled as a nonlinear integer programming problem and solved by a search-based heuristic that minimizes the total number of the dual-gantry cycles. Then, an adaptive clustering approach is developed to allocate components to each gantry cycle by evaluating the gantry traveling distances over the PCB and the component feeders. Numerical experiments compare the proposed ACGA to another clustering-based genetic algorithm LCO and a heuristic algorithm mPhase in the literature using 30 industrial PCB samples. The experiment results show that the proposed ACGA algorithm reduces the total gantry moving distance by 5.71% and 4.07% on average compared to the LCO and mPhase algorithms, respectively.     

Applying genetic algorithm for the development of the components-based embedded system

The embedded system is primarily designed for a particular piece of equipment and it varies on a case-by-case basis. The functionality is required to be specific to the equipment and consequently the application domain is limited. The software embedded in the system also faces problem due to the limitation of the hardware capacity. It is necessary for the designers to consider the hardware capacity and software specification simultaneously while an embedded system is developed. If hardware and software are taken into account concurrently, the design applicability and efficiency are decreased. The evolutionary computing (EC), which comprises techniques of evolutionary programming, evolution strategies, genetic algorithms, and genetic programming has been widely used to solve optimization problems for large scale and complex systems. It is capable to escape not only from local optima due to population based approach, but also from unbiased nature, which enables it to perform well in a situation with little domain knowledge. Therefore, this study proposes an evolutionary approach that applies the characteristics of software reuse, the metrics for the object-oriented concept, and the genetic algorithm to effectively manage and optimize the embedded system. This approach is implemented in the World Wide Web environment. Numerous results associated with performance enhancements of the algorithm are presented in this paper.     

基于改进遗传算法的电力系统无功优化的研究

电力系统无功优化问题是一个多变量、多约束的混合非线性规划问题,其操作变量既有连续变量又有离散变量,其优化过程比较复杂。遗传算法是模拟生物在自然环境中的遗传和进化过程而形成的一种自适应的全局优化搜索算法,可用于解决含有离散变量的复杂优化问题。本文选用遗传算法求解电力系统无功优化问题,并对基本遗传算法的编码、初始种群、适应度函数和交叉、变异策略等进行改进,使用本文提出的改进算法对IEEE1 4节点进行无功优化计算,结果证明本文模型和算法的实用性、可靠性和优越性。     

结合遗传优化的多结构多尺度形态学消噪

传统的形态滤波以及广义形态滤波、自适应加权广义滤波、基于多结构元素的广义形态滤波、基于多方向的广义滤波和基于多尺度的广义滤波在考虑形态学滤波时基本上只兼顾到某一方面或者说只对某一方面的不足进行了改进,不论哪一种滤波方式都没有完全消除噪声。提出了一种基于自适应遗传算法的多结构多尺度形态学滤波方法,主要考虑了滤波窗口的大小、结构元素的种类和方向以及结构元素的优化选择问题,采用遗传算法对结构元素进行优化,并考虑到遗传算法自身的收敛性,采用了保留精英的策略,另外考虑到遗传算法参数的选择问题采用了自适应策略。同时,结合自适应加权广义形态滤波的思想构建基于遗传优化的多结构多尺度自适应加权形态滤波器,滤波效果比传统的形态滤波、广义形态滤波及在此基础上改进的滤波方法效果均好。     

Performance evaluation of a two stage adaptive genetic algorithm (TSAGA) in structural topology optimization

Genetic algorithm with island and adaptive features has been used for reaching the global optimal solution in the context of structural topology optimization. A two stage adaptive genetic algorithm (TSAGA) involving a self-adaptive island genetic algorithm (SAIGA) for the first stage and adaptive techniques in the second stage is proposed for the use in bit-array represented topology optimization. The first stage, consisting a number of island runs each starting with a different set of random population and searching for better designs in different peaks, helps the algorithm in performing an extensive global search. After the completion of island runs the initial population for the second stage is formed from the best members of each island that provides greater variety and potential for faster improvement and is run for a predefined number of generations. In this second stage the genetic parameters and operators are dynamically adapted with the progress of optimization process in such a way as to increase the convergence rate while maintaining the diversity in population. The results obtained on several single and multiple loading case problems have been compared with other GA and non-GA-based approaches, and the efficiency and effectiveness of the proposed methodology in reaching the global optimal solution is demonstrated.     

Multiple-platform based product family design for mass customization using a modified genetic algorithm

A successful product family design method should achieve an optimal tradeoff among a set of conflicting objectives, which involves maximizing commonality across the family of products with the prerequisite of satisfying customers’ performance requirements. Optimization based methods are experiencing new found use in product family design to resolve the inherent tradeoff between commonality and distinctiveness that exists within a product family. This paper presents and develops a 2-level chromosome structured genetic algorithm (2LCGA) to simultaneously determine the optimal settings for the product platform and corresponding family of products, by automatically varying the amount of platform commonality within the product family during a single optimization process. The single-stage approach can yield improvements in the overall performance of the product family compared with two-stage approaches, in which the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage. The augmented scope of 2LCGA allows multiple platforms to be considered during product family optimization, offering opportunities for superior overall design by more efficacious tradeoffs between commonality and performance. The effectiveness of the proposed approach is demonstrated through the design of a family of universal electric motors and comparison against previous work.     

基于动态规划和遗传算法的混合算法研究

动态规划法和遗传算法是目前在水电站厂内经济运行中广泛应用的两种优化算法，文章提出了一种基于动态规划法和遗传算法的混合优化算法来分别解决大规模机组组合问题中空间最优化和时间最优化的计算机求解问题。避免了遗传算法计算速度缓慢的问题，又避免了动态规划法的“维数灾”问题。最后使用清江隔河岩水电站的4台机组的运行数据进行了仿真研究，并和完全使用动态规划法的结果进行了比较，获得了良好的效果，说明该混合优化算法对于厂内经济运行是一种可行的算法。     

A hybrid genetic algorithm for the single machine scheduling problem with sequence-dependent setup times

This paper presents a hybrid approach based on the integration between a genetic algorithm (GA) and concepts from constraint programming, multi-objective evolutionary algorithms and ant colony optimization for solving a scheduling problem. The main contributions are the integration of these concepts in a GA crossover operator. The proposed methodology is applied to a single machine scheduling problem with sequence-dependent setup times for the objective of minimizing the total tardiness. A sensitivity analysis of the hybrid approach is carried out to compare the performance of the GA and the hybrid genetic algorithm (HGA) approaches on different benchmarks from the literature. The numerical experiments demonstrate the HGA efficiency and effectiveness which generates solutions that approach those of the known reference sets and improves several lower bounds.     

基于蜜蜂双种群进化机制的云自适应遗传算法

为了提高传统自适应遗传算法的鲁棒性,受蜜蜂双种群进化的机制启发,把雄蜂通过竞争参与交叉及雄蜂与决定双蜂群优秀遗传基因的蜂后交叉的机制引入算法中,再利用正态云模型云滴的随机性和稳定倾向性特点,提出了基于蜜蜂双种群进化机制的云自适应遗传算法。算法由正态云模型的Y条件云发生器及蜂后参与的方式实现交叉操作,基本云发生器实现变异操作。函数优化实验和暴雨强度公式参数优化的仿真结果表明了算法的有效性和可行性。     

基于进化个体适应值灰度的自适应交互式遗传算法

为将交互式遗传算法成功应用于复杂优化问题,有必要提高交互式遗传算法的性能。提出基于进化个体适应值灰度的交互式遗传算法,该算法采用灰度衡量进化个体的适应值评价不确定性;通过适应值区间的分析,提取反映进化种群分布的信息;基于此,给出了进化个体的交叉和变异概率。将该算法应用于服装进化设计系统,结果表明该算法在每代可以获取更多的满意解。     

Two fast tree-creation algorithms for genetic programming

Genetic programming is an evolutionary optimization method that produces functional programs to solve a given task. These programs commonly take the form of trees representing LISP s-expressions, and a typical evolutionary run produces a great many of these trees. For this reason, a good tree-generation algorithm is very important to genetic programming. This paper presents two new tree-generation algorithms for genetic programming and for “strongly typed” genetic programming, a common variant. These algorithms are fast, allow the user to request specific tree sizes, and guarantee probabilities of certain nodes appearing in trees. The paper analyzes these two algorithms, and compares them with traditional and recently proposed approaches     

一种遗传算法适应度函数的改进方法

针对简单遗传算法中线性适应度函数随进化过程恒定不变的缺点。提出一种可随进化代数动态调整的非线性适应度函数。以典型的遗传算法测试函数为算例，分别以Goldberg提出的线性拉伸方法与文中提出的改进遗传算法进行计算。计算结果表明文中提出的动态适应度函数对简单遗传算法的改进有较明显的效果。     

Combining genetic algorithms with BESO for topology optimization

This paper proposes a new algorithm for topology optimization by combining the features of genetic algorithms (GAs) and bi-directional evolutionary structural optimization (BESO). An efficient treatment of individuals and population for finite element models is presented which is different from traditional GAs application in structural design. GAs operators of crossover and mutation suitable for topology optimization problems are developed. The effects of various parameters used in the proposed GA on the optimization speed and performance are examined. Several 2D and 3D examples of compliance minimization problems are provided to demonstrate the efficiency of the proposed new approach and its capability of obtaining convergent solutions. Wherever possible, the numerical results of the proposed algorithm are compared with the solutions of other GA methods and the SIMP method.     

On a cuckoo search optimization approach towards feedback system identification

This paper presents a cuckoo search algorithm (CSA) based adaptive infinite impulse response (IIR) system identification scheme. The proposed scheme prevents the local minima problem encountered in conventional IIR modeling mechanisms. The performance of the new method has been compared with that obtained by other evolutionary computing algorithms like genetic algorithm (GA) and particle swarm optimization (PSO). The superior system identification capability of the proposed scheme is evident from the results obtained through an exhaustive simulation study.     

An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.     

改进的小生境混合遗传算法在函数优化上的应用

为了提高经典小生境遗传算法的收敛性能,加强局部寻优能力,设计了一种新的小生境混合遗传算法.通过判断算法的在线性能指标Xe(s),将模拟退火算法巧妙地融入算法的后期,并针对小生境遗传算法的特点选用格雷码编码,同时设计了自适应的遗传交叉算子.用一个Shubert多峰值函数对改进的算法进行验证,结果表明:新算法的收敛性能和进化效率得到提高,局部寻优能力也有加强.     

Solving facilities location problem in the presence of alternative processing routes using a genetic algorithm

Facilities location problem deals with the optimization of location of manufacturing facilities like machines, departments, etc. in the shop floor. This problem greatly affects performance of a manufacturing system. It is assumed in this paper that there are multiple products to be produced on several machines. Alternative processing routes are considered for each product and the problem is to determine the processing route of each product and the location of each machine to minimize the total distance traveled by the materials within the shop floor. This paper presents a mixed-integer non-linear mathematical programming formulation to find optimal solution of this problem. A technique is used to linearize the formulated non-linear model. However, due to the NP-hardness of this problem, even the linearized model cannot be optimally solved by the conventional mathematical programming methods in a reasonable time. Therefore, a genetic algorithm is proposed to solve the linearized model. The effectiveness of the GA approach is evaluated with numerical examples. The results show that the proposed GA is both effective and efficient in solving the attempted problem.     

基于科学计算语言的遗传算法工具箱

该文介绍了一个基于科学计算语言的遗传算法工具箱GATS.与现有的基于科学计算语言的遗传算法工具箱相比,GATS在功能上和使用上具有更多的优越性.GATS可支持四种基因编码方式;支持小生境、尺度变换等功能;支持自适应遗传算法、分层遗传算法;支持多目标优化;支持并行处理（Linux/Unix平台）;以及更多的遗传算子等等.GATS采用“主体框架 ＋ 可替换模块”的结构,便于用户加以扩展;带有Tcl/Tk编写的用户界面,使用简单;软件开放源码,特别适用于科研和教育领域.该文详细介绍了GATS的结构、功能和使用方法,并给出了多个应用示例.     

多细胞基因表达式编程的函数优化算法

针对处理复杂的函数优化问题时传统演化算法易出现收敛性能不佳、搜索冗长和精度不高等问题,提出了一种基于多细胞基因表达式编程的函数优化新算法.该算法引入了同源基因和细胞系统思想,设计了相应新的个体编码方案、种群生成和遗传操作策略.通过对8个Benchmarks函数的对比实验,验证了该算法具有很强的全局寻优能力、较佳的收敛性能和更高的解精度.