Optimization with Genetic Algorithms and Splines as a way for Computer Aided Innovation

Abstract

This paper describes the conceptual foundations to construct a method on Computer Aided Innovation for product development. It begins with a brief recap of the different methodologies and disciplines that build its bases. Evolutionary Design is presented and explained how the first activities in Genetic Algorithms (GAs) helped to produce computer shapes that resembled a creative behavior. A description of optimization processes based on Genetic Algorithms is presented, and some of the genetic operators are explained as a background of the creative operators that are intended to be developed. A summary of some Design Optimization Systems is also explained and its use of splined profiles to optimize mechanical structures. The approach to multi-objective optimization with Genetic Algorithms is analyzed from the point of view of Pareto diagrams. It is discussed how the transition from a multi-objective optimization conflict to a solution with the aim of an ideal result can be developed means the help of TRIZ (Theory of Inventive Problem Solving), complementing the discipline of Evolutionary Design. Similarities between Genetic Algorithms and TRIZ regarding ideality and evolution are identified and presented. Finally, a brief presentation of a case study about the design of engine crankshafts is used to explain the concepts and methods deployed. The authors have been working on strategies to optimize the balance of a crankshaft using CAD and CAE software, splines, Genetic Algorithms, and tools for its integration [1] [2].

Domain Reconstruction and Mutation Approach to Expand Creative Design Solution Space for Practical Mechanical Systems

“…The mutation operation in the genetic algorithm for optimization problems [34,35] refers to the random changing of the value ranges of variables to expand the value spaces of optimization variables, prevent the local convergence of optimization problems, and get globally optimal results. However, here, the mutation is the operation of changing the kinematic types of basic operation actions in a subset to different types according to the mutation rules, without changing the kinematic behaviors of the subset, thereby obtaining creative conceptual design results.…”

“…Viewing conceptual design as a process of coevolution of both function structures and means combinations across different levels of an abstraction hierarchy, Jin and Li [33] proposed a grammar-based hierarchical coevolutionary approach to support designers generating design concepts. Three genetic operations, namely, reproduction, crossover, and mutation of the genetic algorithm [34,35], are utilized to evaluate function structures, and their corresponding solution means. Evaluating the fitness of a given function structure is a major challenge because little quantitative information is available, although two subjective terms, feasibility, and desirability, were introduced to scale the fitness in their research.…”

CAI for Minimizing Movement of Solar Tracking Concentrators

“…Another advantage that has lately been valued is the possibility of creating new options in ways that weren't imagined before. According to Albers et al, computational tools can help in the generation of new solutions emerging from many disconnected ideas, and suggesting new concepts [4].…”

“…Nevertheless, a tracking system that can chase the sun rays to obtain heat is ultimately indispensable. Studies for that particular part of the system can be found in [1], [2], [3], [4], [5] and [6]. The literature has as common denominator: the optimization of the system through a reduction of the periodic movement or else through an improvement of the electro-mechanical systems that enable an appropriate orientation.…”

Early Robust Design Approach for Accelerated Automotive Innovation Processes

“…Thus, the systematic robustness evaluation of step 3 has to be done after every optimization step. Regarding minimal influence of the robustness of the optimization algorithm itself, genetic algorithms as evolutionary computational tools fit best as they showed good results in related works [1].…”