Bio-inspired resource allocation optimization using evolution-based genetic algorithm for vocational education skill development: A natural selection approach
Abstract
This study proposes a genetic algorithm-based optimization approach for resource allocation in vocational education skill development processes. The research addresses the critical challenge of efficiently distributing limited educational resources while maximizing learning outcomes and maintaining operational constraints. Through systematic implementation and rigorous evaluation, we developed a multi-objective optimization model incorporating educational effectiveness, resource utilization efficiency, and distribution equity considerations. The genetic algorithm demonstrated superior performance with a 27.3% improvement in resource utilization efficiency compared to traditional methods and achieved a 92.3% average goal satisfaction rate across defined targets. Experimental results across 12 vocational institutions show significant improvements in key performance indicators, including a 23.7% increase in equipment utilization rates and an 18.9% enhancement in instructor resource efficiency. Statistical analysis confirms the significance of these improvements (p < 0.001). The proposed approach consistently outperformed other contemporary optimization algorithms in terms of convergence speed, solution quality, and robustness across different problem scales. This research contributes to both theoretical understanding and practical implementation of resource optimization in vocational education, providing a robust framework for enhancing educational effectiveness through intelligent resource allocation.
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