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Balanced Allocation of Educational Resources Based on Parallel Genetic Algorithm

机译:基于平行遗传算法的教育资源均衡分配

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摘要

Higher education is one of the scarcest social resources, with high demand and low supply, and higher education is currently in limited supply in our country, making it difficult to resolve this contradiction. In addition to increasing investment in higher education as much as possible, the most important thing is to maximize the benefits of education through the rational allocation of resources. An evaluation index system of educational resource input-output was constructed, and a multiobjective function model of educational resource utilization efficiency and allocation efficiency was proposed. We should rationalize the allocation of resources and maximize the benefits of innovation and entrepreneurship education in colleges and universities. By combining particle swarm optimization with genetic algorithm, we can simulate and solve the model. The simulation results suggest that by optimizing the usage and allocation efficiency of innovation, it may be increased. College and university entrepreneurship education resources have increased by 18.72 percent and 20.98 percent, respectively, on average, and tend to be in a balanced state, which can realize the optimization of education resources allocation.
机译:高等教育是社会资源最稀缺的领域之一,需求量大,供给低,目前我国高等教育供不应求,这一矛盾难以解决。除了尽可能增加对高等教育的投入外,最重要的是通过合理配置资源,实现教育效益的最大化。构建教育资源投入产出评价指标体系,提出教育资源利用效率和配置效率的多目标函数模型。要合理配置资源,实现高校创新创业教育效益最大化。通过将粒子群优化与遗传算法相结合,我们可以对模型进行仿真和求解。仿真结果表明,通过优化创新的利用和配置效率,可以提高创新的利用和配置效率。高校创业教育资源平均分别增长18.72%和20.98%,且趋于均衡,可实现教育资源配置的优化。

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