An artificial-intelligence-driven product design framework with a synergistic combination of Genetic Algorithm and Particle Swarm Optimization

• Published in: Soft computing (Berlin, Germany) Germany), 2023-12, Vol.27 (23), p.17621-17638
• Main Authors: Liu, Yuge, Kim, KieSu
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Artificial neural networks; Collaboration; Computational Intelligence; Context; Control; Data processing; Decision making; Engineering; Genetic algorithms; Innovations; Leather; Machine learning; Mathematical Logic and Foundations; Mechatronics; Modernization; Optimization; Particle swarm optimization; Product design; Robotics; Seats; Swarm intelligence
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-023-09223-4

In recent years, China's rapid advancements in artificial intelligence (AI) technology have positioned it as a global leader in information processing, intelligent chip development, and deep learning technologies. The pervasive impact of AI has led to transformative shifts in various domains of life, with its integration across industries substantially enhancing overall production efficiency. Particularly evident in product design, the infusion of AI technology has catalyzed significant productivity improvements. This research investigates the amalgamation of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) within the framework of AI technology to establish a robust product design system. The system aims to enhance the efficiency and efficacy of design processes by leveraging the complementary strengths of GA and PSO. This integration has propelled product design into accelerated evolution and modernization. The study delves into AI's application of deep neural networks in product design within the context of the digital age. Artificial intelligence technology is used in product design in the context of the Internet. First, the concept of AI technology is briefly introduced, and DNN algorithms are described in detail, including the PSO and Genetic Algorithms. Second, the product design and development process based on artificial intelligence technology, design guidelines, and technical features are mainly described. Finally, using the seat as the research object, the application effect of artificial intelligence technology in product design is analyzed, and 10 subjective evaluation questions are set to experiment. Regarding material texture, the highest score for the B3 Barcelona chair is 4.3. Users prefer leather seats with diverse materials, and the score for painted wood materials is lower. From the product structure and shape analysis, the highest score is the B3 Barcelona chair, which has a value of 4.1, and the lowest score is the B1 red and blue chair, which has a value of 3.0. Based on this result, users like arc and curve shape much more than straight structures.