Pork accounts for a high proportion of the Chinese population’s meat consumption, and imported pork is heavily traded, reducing supply of domestic pork in the face of continued demand. Global pork markets are becoming more competitive, riding the wind of the bilateral free trade agreement. The World Food and Agriculture Organization (FAO) compiles prices for other major food categories but does not track changes in the imported pork prices in China. This study has filled this gap by designing the Imported Pork Producer Declaration Price Index (PPI_IPD). Using the well-known Producer Price Index (PPI) model, PPI_IPD is based on the data from Chinese customs import declarations, which has high reliability and reasonableness. For this reason, the index can help governments, enterprises, analysts, and others to conduct analysis for imported pork prices in China and avoid international trade risks. The findings show that proposed PPI_IPD is highly correlated with the Chinese domestic pork market and the pork price industry stock market. The index helps monitor changes in international pork prices and is an effective tool for analyzing and controlling trade risks.
Metaheuristic algorithms are an important area of research in artificial intelligence. The tumbleweed optimization algorithm (TOA) is the newest metaheuristic optimization algorithm that mimics the growth and reproduction of tumbleweeds. In practice, chaotic maps have proven to be an improved method of optimization algorithms, allowing the algorithm to jump out of the local optimum, maintain population diversity, and improve global search ability. This paper presents a chaotic-based tumbleweed optimization algorithm (CTOA) that incorporates chaotic maps into the optimization process of the TOA. By using 12 common chaotic maps, the proposed CTOA aims to improve population diversity and global exploration and to prevent the algorithm from falling into local optima. The performance of CTOA is tested using 28 benchmark functions from CEC2013, and the results show that the circle map is the most effective in improving the accuracy and convergence speed of CTOA, especially in 50D.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.