Optimization of electronics component placement design on PCB using self organizing genetic algorithm (SOGA)
Optimization of electronics component placement design on PCB using self organizing genetic algorithm (SOGA)
复制标题
使用自组织遗传算法 (SOGA) 优化 PCB 上的电子元件布局设计
DOI：
10.1007/s10845-010-0444-x
复制
发表时间：
2010
影响因子：
8.3
通讯作者：
M. Khalid
中科院分区：
文献类型：
--
作者：
F. S. Ismail;R. Yusof;M. Khalid
电子组件在印刷电路板（PCB）上的最佳放置需要满足多个冲突的设计目标，因为大多数组件都具有不同的功率散失，工作温度，材料类型和尺寸。此外，大多数电子公司目前都强调设计较小的包装电子系统以提高系统性能。本文提出了一种新的自组织遗传算法（SOGA）方法，用于解决此多目标优化问题。 SOGA可以看作是两种气体的级联，由两个步骤的健身评估过程组成，以确保最佳选择所选染色体的适应性。该算法是基于加权总和方法遗传算法（WSGA）开发的，其中使用内部循环GA来优化WSGA的权重选择。进行实验以评估SOGA的性能。在实验中提出了四个目标函数，这些函数是组件的温度，PCB面积，高功率组件放置和高潜在的关键组件距离。 SOGA性能的比较是用两种众所周知的固定重量GA（FWGA）和随机加权GA（RWGA）进行的。结果表明，与其他方法相比，SOGA给出了更好的最佳解决方案。
The optimal placement of electronic components on a printed circuit board (PCB) requires satisfying multiple conflicting design objectives as most of the components have different power dissipation, operating temperature, types of material and dimension. In addition, most electronic companies are currently emphasizing on designing a smaller package electronic system in order to increase the system performance. This paper presents a new self organizing genetic algorithm (SOGA) method for solving this multi-objective optimization problem. The SOGA can be viewed as a cascade of two GAs which consists of two steps fitness evaluation process to ensure that the fitness of selected chromosomes for each iteration process is optimally selected. The algorithm is developed based on weighted sum approach genetic algorithm (WSGA) where an inner loop GA is used to optimize the selection of weights of the WSGA. Experiments are conducted to evaluate the performance of SOGA. Four objective functions are formulated in the experiments which are temperature of components, area of PCB, high power component placement and high potential critical components distance. Comparisons of the performance of SOGA are made with two well known methods namely fixed weight GA (FWGA) and random weighted GA (RWGA). The results show that the SOGA gives a better optimal solution as compared to the other methods.