In this paper, a new grey wolf optimizer (GWO) variant based on a novel weighted distance (WD) called the GWO-WD algorithm is presented to solve global optimization problems. First, a modified position-updating equation formulated using the proposed strategy is employed to obtain additional information and improved global solutions. Then, several of the worst individuals are eliminated and repositioned using an elimination and repositioning strategy to improve the capability of the algorithm and avoid falling into local optima. The performance of the algorithm is verified by utilizing 23 widely used benchmark test functions, the IEEE CEC2014 test suite and three well-known engineering design problems. The simulation results of the proposed algorithm are compared with those of the standard GWO algorithm, three GWO variants and several existing methods, and the proposed algorithm is revealed to be very competitive and, in many cases, superior.

With the rapid development of information technology, the sharing economy based on “Internet plus” cloud platforms has become a new collaborative innovation mode and a hot topic in recent years. Considering that government regulation restricts green innovation cooperation among cloud manufacturing enterprises, an evolutionary game model involving the government and cloud manufacturing enterprises A and B with potential differences in their technology knowledge is established using evolutionary game theory. A replication dynamic equation is established, the evolutionarily stable equilibrium strategy of the three parties is analysed, and the key factors affecting the cooperative selection strategy of the government and cloud manufacturing enterprises are discussed through a MATLAB-based numerical simulation. This research shows that when governmental incentives and punishments, the platform load capacity, the trust between enterprises, the technology loss coefficient, and the informatization degree are increased, the government will tend to choose supervision, and cloud manufacturing enterprises A and B will tend to choose the “collaborative innovation” strategy. These results provide a scientific basis suggesting that the government should not only formulate rules and regulations for cloud manufacturing enterprises but also promote green collaborative innovation among such enterprises and enhance their core competitiveness.

Information collaboration is a core driver of digital transformation and efficiency improvement in agri-food supply chains. This study constructs a quadripartite evolutionary game model involving the government, an information service platform, farmers, and agri-food enterprises. By integrating system dynamics, it analyzes stakeholders’ strategic interactions and evolutionary pathways while exploring the regulatory effects of key parameters in reward and penalty mechanisms on system convergence. The key findings are as follows: (1) The system reaches a stable equilibrium regardless of initial strategy combinations. (2) The reward–penalty mechanism is essential for equilibrium stability, but the reward amount and allocation ratios must meet threshold constraints. (3) Given the significant path-dependent lock-in effect in agri-food enterprises’ strategy convergence under static parameters, a dynamic parameter configuration scheme is proposed to reshape convergence and optimize equilibrium. The simulation results indicate that dynamic parameter regulation sacrifices the regulatory efficiency of the information service platform to enhance the overall collaboration. A joint dynamic reward–penalty strategy improves efficiency but delays platform convergence, whereas a single dynamic incentive offers a balanced trade-off. Based on this, an incentive framework is developed to guide government incentive design. This study expands the theoretical framework of information collaboration in AFSCs and provides practical guidance for policymakers.