Abstract
For the application of the standard genetic algorithm in illustration art design, there are still problems such as low search efficiency and high complexity. This paper proposes an illustration art design model based on operator and clustering optimization genetic algorithm. First, during the operation of the genetic algorithm, the values of the crossover probability and the mutation probability are dynamically adjusted according to the characteristics of the population to improve the search efficiency of the algorithm, then the k-medoids algorithm is introduced to optimize the clustering of the genetic algorithm, and a cost function is used to carry out and evaluate the quality of clustering to optimize the complexity of the original algorithm. In addition, a multiobjective optimization genetic algorithm with complex constraints based on group classification is proposed. This algorithm focuses on the problem of group diversity and uses k-means cluster analysis operation to solve the problem of group diversity. The algorithm divides the entire group into four subgroups and assigns appropriate fitness values to reflect the optimal preservation strategy. A large number of computer simulation calculations show that the algorithm can obtain a widely distributed and uniform Pareto optimal solution, the evolution speed is fast, usually only a few iterations can achieve a good optimization effect, and finally the improved genetic algorithm is used to design the random illustration art. The example simulation shows that the improved algorithm proposed in this paper can achieve higher artistic and innovative illustration art design.
Highlights
In the traditional art design process, if designers have inspiration and ideas, they must use tools to transfer their designs to reality, which requires human labor, so the quantity and quality of creations are limited
Computer simulation experiments show that the algorithm can obtain the widely distributed and uniform Pareto optimal solution and has a fast evolution speed and usually only needs 1040 generations to achieve a good optimization effect. e first part is the introduction. e second part introduces illustration design based on genetic operator optimization. e third part carries out multiobjective clustering optimization genetic algorithm based on Pareto optimal solution illustration design. e fourth part is the example verification. e fifth part is the conclusion
Computer technology is advancing on a global scale, and the use of evolutionary technology to help product innovation and design is an important approach
Summary
In the traditional art design process, if designers have inspiration and ideas, they must use tools to transfer their designs to reality, which requires human labor, so the quantity and quality of creations are limited. When the given gene data set changes slightly, the hierarchical clustering algorithm is easy to interfere, the clustering process lacks robustness, and the clustering results will greatly deviate Kmeans clustering is another method often used for gene expression data analysis due to its good computational performance [19]. En, on the basis of GKA, the algorithm was improved, and the incremental genetic k-mean clustering algorithm [27] was proposed, which was applied to the clustering analysis of gene expression data and achieved good results. In order to optimize the standard genetic algorithm and improve some existing shortcomings, this paper uses a design model of illustration art, which is based on the genetic algorithm of cluster optimization and operator This model is more innovative through experimental simulation. Computer simulation experiments show that the algorithm can obtain the widely distributed and uniform Pareto optimal solution and has a fast evolution speed and usually only needs 1040 generations to achieve a good optimization effect. e first part is the introduction. e second part introduces illustration design based on genetic operator optimization. e third part carries out multiobjective clustering optimization genetic algorithm based on Pareto optimal solution illustration design. e fourth part is the example verification. e fifth part is the conclusion
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
