Application of Joint Improvement of Ant Colony Algorithm and Consensus Proactivity in Swarm Intelligence Robot System Manufacturing Industry

Supply chain management capability is a key factor for enterprises to remain competitive in the globalized market. It supports the development of the manufacturing industry and is an organic whole formed by the interaction and integration of various types of cooperative and interdependent modules. However, the effectiveness of the manufacturing supply chain is facing great challenges, including a lack of real-time data sharing and analysis, and difficulty in efficiently completing collaborative tasks. To solve the above problems, the study first accomplishes the morphology generation and transformation of group intelligent robots on the basis of dual-layer gene regulatory network. Then, the traveler problem is solved by ant colony optimization algorithm, and stigmergy is optimally introduced to construct the intelligent system collaborative search and fencing method. The research results indicated that the research method was significantly superior to other methods in the statistical analysis of various performance indicators, and the comparison had significant statistical significance ([Formula: see text] ¡ 0.05), which ensured the reliability and reproducibility of the results. In the actual warehouse environment management, the optimal number of robots for multi-objective tasks in the research method was 18, and the successful capture coverage rate reached 100% in the end. Compared with the swarm intelligence robot system based on adaptive distributed control and the swarm intelligence robot system based on improved tunicate swarm algorithm, the average capture distance of the research system was reduced by 14.81% and 22.58%, respectively. The above results indicate that the research method can significantly improve the collaborative task performance of the manufacturing supply chain and promote the sustainable development of the manufacturing industry.