Labor Market Adaptive Skill Transition Optimization: A Formalized Algorithmic Framework Inspired by Labor Market & Skill Transition Research
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901819667513344 |
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| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>Driven by technological change, industrial upgrading, and employment structure migration, the labor market exhibits high dynamism. Traditional optimization algorithms often lack a model-based expression of characteristics such as "changes in occupational skills," "labor transferability," and "mismatch between skill supply and demand." This paper proposes a novel heuristic optimization algorithm model for skill transfer based on research on the Labor Market & Skill Transition (Labor Market & Skill Transition)—the Labor Market Adaptive Dynamic Skill Transfer Optimization Algorithm (LMSTO). The algorithm is based on three core ideas: (1) the labor skill transfer trajectory serves as the search trajectory in the solution space; (2) the skill matching degree function serves as the construction principle of the fitness function; and (3) the group conducts a migration-style search within the "occupation cluster structure."</span></p> <p> </p> <p><span>This paper innovatively proposes mechanisms such as the "Skill Mobility Matrix," "Demand Gradient Drive," "Labor Friction Energy," and "Occupation Cluster Drift," and constructs a large number of pure text mathematical formulas to describe the complete optimization search process. This algorithm has an independent system that is significantly different from existing intelligent optimization methods, and can be used in fields such as combinatorial optimization, structure search, task allocation, and resource scheduling.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17920622 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Labor Market Adaptive Skill Transition Optimization: A Formalized Algorithmic Framework Inspired by Labor Market & Skill Transition Research Zhang, Jincheng <p><span>Driven by technological change, industrial upgrading, and employment structure migration, the labor market exhibits high dynamism. Traditional optimization algorithms often lack a model-based expression of characteristics such as "changes in occupational skills," "labor transferability," and "mismatch between skill supply and demand." This paper proposes a novel heuristic optimization algorithm model for skill transfer based on research on the Labor Market & Skill Transition (Labor Market & Skill Transition)—the Labor Market Adaptive Dynamic Skill Transfer Optimization Algorithm (LMSTO). The algorithm is based on three core ideas: (1) the labor skill transfer trajectory serves as the search trajectory in the solution space; (2) the skill matching degree function serves as the construction principle of the fitness function; and (3) the group conducts a migration-style search within the "occupation cluster structure."</span></p> <p> </p> <p><span>This paper innovatively proposes mechanisms such as the "Skill Mobility Matrix," "Demand Gradient Drive," "Labor Friction Energy," and "Occupation Cluster Drift," and constructs a large number of pure text mathematical formulas to describe the complete optimization search process. This algorithm has an independent system that is significantly different from existing intelligent optimization methods, and can be used in fields such as combinatorial optimization, structure search, task allocation, and resource scheduling.</span></p> |
| title | Labor Market Adaptive Skill Transition Optimization: A Formalized Algorithmic Framework Inspired by Labor Market & Skill Transition Research |
| url | https://doi.org/10.5281/zenodo.17920622 |