An intelligent crossover methodology within the genetic algorithm (GA) is explored within both mathematical and finite element arenas improving both design and solution convergence time. This improved intelligent cros...An intelligent crossover methodology within the genetic algorithm (GA) is explored within both mathematical and finite element arenas improving both design and solution convergence time. This improved intelligent crossover outperforms the traditional genetic algorithm combined with a rule-based approach utilizing domain specific knowledge developed by Webb, et al. [1]. The encoding of the improved crossover consists of two chromosome strings within the genetic algorithm where the first string represents the design or solution string, and the second string represents chromosome crossover string intelligence. This improved crossover methodology saves the best population members or designs evaluated from each generation and applies crossover chromosome intelligence to the best saved population members paired with globally selected parents. Enhanced features of this crossover methodology employ the random selection of the best designs from the prior generation as a potential parent coupled with alternating intelligence pairing methods. In addition to this approach, two globally selected parents possess the ability to mate utilizing crossover chromosome string intelligence maintaining the integrity of a global GA search. Overall, the final population following crossover employs both global and best generation design chromosome strings to maximize creativity while enhancing the solution search. This is a modification to a conventional GA that can be translated into GA encoding. This technique is explored initially through a Base 10 mathematical application followed by the examination of plate structural optimization considering stress and displacement constraints. Results from crossover intelligence are compared with the conventional genetic algorithm and from Webb, et al. [1] which illustrates the outcome of a two phase genetic optimization algorithm.展开更多
基于方位特征(Position and Orientation Characteristic,POC)集方程的并联机构设计理论,设计了一种具有符号式位置正解并且存在部分运动解耦特性的两平移一转动(2T1R)并联机构。对该机构进行了方位特征集、自由度以及耦合度等拓扑性能...基于方位特征(Position and Orientation Characteristic,POC)集方程的并联机构设计理论,设计了一种具有符号式位置正解并且存在部分运动解耦特性的两平移一转动(2T1R)并联机构。对该机构进行了方位特征集、自由度以及耦合度等拓扑性能的分析;基于拓扑结构运动学建模原理,求解了该机构的解析式位置正解、解析式逆解,并通过位置逆解进行了验算,证明了该机构具有弯曲平移的运动特性。传统的只有平直运动的2T1R并联机构要实现曲线平移,需要将多个平直移动通过复杂算法进行曲线拟合,而该机构只需通过一维弯曲平移即可实现,极大降低了机构的复杂程度及控制算法的难度。展开更多
A topological structural design approach is presented which is based upon the implementation of a two phase evolutionary optimization algorithm in conjunction with a finite element analysis code. The first phase utili...A topological structural design approach is presented which is based upon the implementation of a two phase evolutionary optimization algorithm in conjunction with a finite element analysis code. The first phase utilizes a conventional genetic approach which performs a global search for the optimal design topology. Dual level material properties are specified within the genetic encoding and are applied to each individual element in the design mesh to represent either design material or a void. The second phase introduces a rule based refinement which allows for user design intent to accelerate the solution process and eliminate obvious design discrepancies resulting from the phase one search. A series of plate design problems are presented where the objective is to minimize the overall volume of the structure under predefined loading and constraint conditions. The constraints include both stress and deflection considerations where stress is calculated through the use of a commercial finite element package. The initial plate example incorporates a coarse mesh, but a gradual decrease in element size was employed for the remaining cases examined. Replacement of the phase one search with a set of randomly generated designs is demonstrated in order to form a greatly reduced design space which drastically increases the efficiency of the solution process. Comparison results are drawn between the conventional genetic algorithm and the two phase procedure.展开更多
文摘An intelligent crossover methodology within the genetic algorithm (GA) is explored within both mathematical and finite element arenas improving both design and solution convergence time. This improved intelligent crossover outperforms the traditional genetic algorithm combined with a rule-based approach utilizing domain specific knowledge developed by Webb, et al. [1]. The encoding of the improved crossover consists of two chromosome strings within the genetic algorithm where the first string represents the design or solution string, and the second string represents chromosome crossover string intelligence. This improved crossover methodology saves the best population members or designs evaluated from each generation and applies crossover chromosome intelligence to the best saved population members paired with globally selected parents. Enhanced features of this crossover methodology employ the random selection of the best designs from the prior generation as a potential parent coupled with alternating intelligence pairing methods. In addition to this approach, two globally selected parents possess the ability to mate utilizing crossover chromosome string intelligence maintaining the integrity of a global GA search. Overall, the final population following crossover employs both global and best generation design chromosome strings to maximize creativity while enhancing the solution search. This is a modification to a conventional GA that can be translated into GA encoding. This technique is explored initially through a Base 10 mathematical application followed by the examination of plate structural optimization considering stress and displacement constraints. Results from crossover intelligence are compared with the conventional genetic algorithm and from Webb, et al. [1] which illustrates the outcome of a two phase genetic optimization algorithm.
文摘基于方位特征(Position and Orientation Characteristic,POC)集方程的并联机构设计理论,设计了一种具有符号式位置正解并且存在部分运动解耦特性的两平移一转动(2T1R)并联机构。对该机构进行了方位特征集、自由度以及耦合度等拓扑性能的分析;基于拓扑结构运动学建模原理,求解了该机构的解析式位置正解、解析式逆解,并通过位置逆解进行了验算,证明了该机构具有弯曲平移的运动特性。传统的只有平直运动的2T1R并联机构要实现曲线平移,需要将多个平直移动通过复杂算法进行曲线拟合,而该机构只需通过一维弯曲平移即可实现,极大降低了机构的复杂程度及控制算法的难度。
文摘A topological structural design approach is presented which is based upon the implementation of a two phase evolutionary optimization algorithm in conjunction with a finite element analysis code. The first phase utilizes a conventional genetic approach which performs a global search for the optimal design topology. Dual level material properties are specified within the genetic encoding and are applied to each individual element in the design mesh to represent either design material or a void. The second phase introduces a rule based refinement which allows for user design intent to accelerate the solution process and eliminate obvious design discrepancies resulting from the phase one search. A series of plate design problems are presented where the objective is to minimize the overall volume of the structure under predefined loading and constraint conditions. The constraints include both stress and deflection considerations where stress is calculated through the use of a commercial finite element package. The initial plate example incorporates a coarse mesh, but a gradual decrease in element size was employed for the remaining cases examined. Replacement of the phase one search with a set of randomly generated designs is demonstrated in order to form a greatly reduced design space which drastically increases the efficiency of the solution process. Comparison results are drawn between the conventional genetic algorithm and the two phase procedure.
