Abstract
Abstract The ant colony algorithm, a heuristic algorithm that mimics ants’ foraging, can optimize problem-solving. Applying this algorithm to the design of innovation and entrepreneurship projects in colleges and universities can enhance the effectiveness and feasibility of these projects. Based on an analysis of the college innovation and entrepreneurship project design process, the article explores the entrepreneurial path through the use of big data analysis. Then, it discusses the ant colony algorithm’s basic concept and mathematical model and proposes a two-population ant colony algorithm for multi-objective resource-constrained project scheduling problems. When compared to the traditional algorithm, the improved ant colony algorithm runs 25–33 ms faster, and Company A’s operating costs drop by 1,176,000 yuan from 2020 to 2023. This shows that the improved ant colony algorithm suggested in this paper can make university innovation and entrepreneurship programs more efficient at allocating resources.
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