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
Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequ...Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequence while minimizing computation time. This combinatorial optimization approach is initially demonstrated by utilizing a traditional genetic algorithm (GA), followed by the incorporation of artificial intelligence utilizing embedded rules based on domain-specific knowledge. The aim of this initiative is to compare the results of the traditional and rule-based optimization approaches with results acquired through an intelligent crossover methodology. The intelligent crossover approach encompasses a two-dimensional GA encoding where a second chromosome string is introduced within the GA, offering a sophisticated means for chromosome crossover amongst selected parents. Additionally, parent selection intelligence is incorporated where the best-traversed paths or population members are retained and utilized as potential parents to mate with parents selected within a traditional GA methodology. A further enhancement regarding the utilization of saved optimal population members as potential parents is mathematically explored within this literature.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
Structural designs (i.e. truss structures) are derived by the use of a three phase genetic optimization approach, where the minimization of volume is the objective of each truss structure considered. A genetic algorit...Structural designs (i.e. truss structures) are derived by the use of a three phase genetic optimization approach, where the minimization of volume is the objective of each truss structure considered. A genetic algorithm is employed which controls the three phase optimization technique. The first phase utilizes the conventional functionality of the genetic algorithm from an evolutionary perspective, however designer interaction by the use of constant rules is provided to ensure an effective evolutionary search outcome. The second phase enhances the best design constructed from phase one by the use of domain specific knowledge in the form of design rules. Phase three improves the final design assembled within phase two by the reduction of truss element areas. This refinement process ensures that the design constraints provided are active, indicating an optimal search solution. All phases operate from a global perspective;however the phase two optimization methodology operates from a more radical approach which encompasses the concept of designing from a “blank sheet of paper” point of view. Results are provided upon the conclusion of each truss example considered which includes the outcomes of each phase for comparison purposes.展开更多
The use of near infrared, high intensity femtosecond laser pulses for the inscription of long period fiber gratings in photonic crystal fiber is reported. The formation of grating structures in photonic crystal fiber ...The use of near infrared, high intensity femtosecond laser pulses for the inscription of long period fiber gratings in photonic crystal fiber is reported. The formation of grating structures in photonic crystal fiber is complicated by the fiber structure that allows wave-guidance but that impairs and scatters the femtosecond inscription beam. The effects of symmetric and asymmetric femtosecond laser inscriptions are compared and the polarization characteristics of long period gratings and their responses to external perturbations are reported.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Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequence while minimizing computation time. This combinatorial optimization approach is initially demonstrated by utilizing a traditional genetic algorithm (GA), followed by the incorporation of artificial intelligence utilizing embedded rules based on domain-specific knowledge. The aim of this initiative is to compare the results of the traditional and rule-based optimization approaches with results acquired through an intelligent crossover methodology. The intelligent crossover approach encompasses a two-dimensional GA encoding where a second chromosome string is introduced within the GA, offering a sophisticated means for chromosome crossover amongst selected parents. Additionally, parent selection intelligence is incorporated where the best-traversed paths or population members are retained and utilized as potential parents to mate with parents selected within a traditional GA methodology. A further enhancement regarding the utilization of saved optimal population members as potential parents is mathematically explored within this literature.
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.
文摘Structural designs (i.e. truss structures) are derived by the use of a three phase genetic optimization approach, where the minimization of volume is the objective of each truss structure considered. A genetic algorithm is employed which controls the three phase optimization technique. The first phase utilizes the conventional functionality of the genetic algorithm from an evolutionary perspective, however designer interaction by the use of constant rules is provided to ensure an effective evolutionary search outcome. The second phase enhances the best design constructed from phase one by the use of domain specific knowledge in the form of design rules. Phase three improves the final design assembled within phase two by the reduction of truss element areas. This refinement process ensures that the design constraints provided are active, indicating an optimal search solution. All phases operate from a global perspective;however the phase two optimization methodology operates from a more radical approach which encompasses the concept of designing from a “blank sheet of paper” point of view. Results are provided upon the conclusion of each truss example considered which includes the outcomes of each phase for comparison purposes.
文摘The use of near infrared, high intensity femtosecond laser pulses for the inscription of long period fiber gratings in photonic crystal fiber is reported. The formation of grating structures in photonic crystal fiber is complicated by the fiber structure that allows wave-guidance but that impairs and scatters the femtosecond inscription beam. The effects of symmetric and asymmetric femtosecond laser inscriptions are compared and the polarization characteristics of long period gratings and their responses to external perturbations are reported.
