Research on Global Illegal Wildlife Trade Based on Integrated Optimization Methodologies

To help improve the situation at this stage of the illegal wildlife trade, this article takes a comprehensive look at global data on Illegal Wildlife Trade (IWT) based on the background that illegal wildlife trade is still rampant at this stage. Through data-driven as well as mathematical modeling, feasible and effective measures are proposed. Firstly, we analyze the massive data through deep learning techniques, and neural networks are used to predict the subsequent trend of illegal wildlife trade. Immediately after that, we give two scenarios. The first is to stimulate people's awareness of protection through global advocacy, we establish a mathematical model of travelers, and the simulated annealing algorithm is used to solve the optimal routes, which gives the specific routes of global advocacy. Secondly, we analyze the important nodes of the global illegal wildlife trade, model the planning problem based on the greedy strategy, establish the global monitoring measures, and give the location of the corresponding monitoring points. Overall, we provide a general assessment of the global illegal wildlife trade and provide specific and feasible means to suppress it.