Abstract
With the rapid development of artificial intelligence, handicraft design has developed from artificial design to artificial intelligence design. Traditional handicraft design has the problems of long time consumption and low output, so it is necessary to improve the process technology. Artificial intelligence technology can provide optimized design steps in handicraft design and improve design efficiency and process level. Handicrafts are regarded as important social products and exist in people’s daily life. In the current society, many people do handicrafts and there are major exhibitions. Furthermore, the display of handicrafts is also very grand and shocking. In the design of handicrafts, the traditional design method cannot completely keep up with the production speed and efficiency of handicrafts. Therefore, this paper adopts the fusion multi-intelligent decision algorithm of multi-node branch design in the design method of handicraft. The algorithm model combination is used to analyze and design the layout of the handicraft, which speeds up the design efficiency and production of the handicraft. In this paper, two intelligent algorithms will be used for fusion; they are genetic algorithm and GA-PSO fusion algorithm obtained by particle swarm optimization and they are embedded in handicraft design method for application through mathematical model construction and function construction. After comparing the performance parameter index data of three intelligent algorithms and GA-PSO fusion algorithm, it is obtained that GA-PSO fusion algorithm is 97% correct and has 82% readability, 72% robustness, and 61% structure, making it have better important indicators. Four algorithms optimize each design problem in all aspects of handicraft design at present. Design efficiency, image distribution rate, image optimization degree, and image clarity are compared by simulation experiments. Compared with three intelligent algorithms, traditional design methods, and manual design methods, GA-PSO fusion algorithm can effectively improve the design method and design effect of handicrafts with 92.1% design efficiency, 82.7% image distribution rate, 94.3% image optimization degree, and 84% layout void rate. Finally, the space complexity experiment of four algorithms shows that GA-PSO algorithm can achieve 9.73 dispersion with 11.42 space complexities, which makes the dimension reduction relatively stable, and the algorithm can maintain stability in the design and application of handicrafts.
Highlights
Decision support system should be able to provide various auxiliary means for decision-making, such as information collection, transmission, and processing
The three intelligent algorithms are compared with GA-Particle swarm optimization (PSO) algorithm, which is a fusion multi-intelligent algorithm for data fusion of genetic algorithm and particle swarm optimization algorithm. e performance parameters of GA-PSO algorithm are compared by simulation experiments. rough the correctness, readability, and robustness of the algorithm, the experimental data are compared with the structural and finite certainty
It is concluded that the GA-PSO fusion algorithm has 97% correctness, 82% readability, 72% robustness, and 61% structure, and the main performance parameters of this algorithm are much higher than those of the other three algorithms, which makes the advantages of the algorithm play a better role in solving some common problems in handicraft design methods
Summary
Decision support system should be able to provide various auxiliary means for decision-making, such as information collection, transmission, and processing. Even in the environment with the same information, different decision-makers may make different decisions, which is related to many factors such as the decision-makers’ attitude towards risks and the decision criteria they adopt. For the decision-making of complex systems or major problems, in order to improve the accuracy and scientificity of decisionmaking, it is often necessary to make group decision-making or multilevel decision-making [1]. Comprehensive application of various criteria can often lead to the most satisfactory decision. Many decisionmakers often have inconsistent decision results due to factors such as information possession and their own characteristics [4]. Many decisionmakers often have inconsistent decision results due to factors such as information possession and their own characteristics [4]. e usual group decision-making theory
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