The development of circular economy and the construction of industrial circular economy are specific and important practical tools to achieve sustainable development, which are rapidly spreading around the world. It is reported that the industrial chain of circular economy is facing more and more practical problems. This paper aimed to study how to apply artificial intelligence to propose optimization strategies for the industrial ecological chain. This paper put forward the optimization strategy of business circular economy management industrial ecological chain. Ant colony algorithm is an intelligent optimization algorithm based on artificial intelligence. This algorithm can not only solve the problem of path optimization, but also apply to the optimization problem of industrial ecological chain. The experiments in this paper's results showed that: when the ant colony algorithm's iterations were 150, the lowest convergence time was 0.49 seconds, and the highest convergence time was 0.56 seconds, as well as the highest convergence rate was 60%; when the iterations were 150, the lowest convergence time was 0.13 seconds, and the highest convergence time was 0.18 seconds, as well as the highest convergence rate was 95%. It was observed that the enhanced ant colony algorithm had the best ability to search for the global optimum and the fastest convergence time, indicating that it could be used to optimize the strategy of locating the ecological chain.

Jun Liu. Optimize the Industrial Ecological Chain of Circular Economy Management in African Enterprises. International Journal of Social Sciences and Economic Management (2023), Vol. 4, Issue 3: 63-78. https://doi.org/10.38007/IJSSEM.2023.040308.

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