Article: Metaheuristic-based talent configuration optimisation for enterprises under the new quality productive forces framework Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.97 pp.41 - 63 Abstract: The optimisation of enterprise talent structure faces challenges of high-dimensional and multiple constraints. Existing meta-heuristic methods mostly focus on a single objective and are unable to balance skills matching, generational balance, and strategic resilience. This paper proposes a person-job optimisation model based on particle swarm optimisation, which objectively assigns weights using the entropy weight method and introduces a penalty function to handle multi-objective conflicts. On a real human resource dataset from an enterprise, the area under the curve of person-job optimisation model based on particle swarm optimisation reached 0.94, which was 14.6% higher than that of traditional particle swarm optimisation; the talent-job matching degree increased from the initial 0.51 to 0.88, and the generational coupling coefficient reached 0.82. This model fills the gap in methods for the collaborative optimisation of multi-dimensional talent structures under the requirements of new quality productivity. Inderscience Publishers - linking academia, business and industry through research

Title: Metaheuristic-based talent configuration optimisation for enterprises under the new quality productive forces framework

Authors: Leimei Ai

Addresses: Employment Department, Ganxi Vocational College of Science and Technology, Xinyu, 338025, China

Abstract: The optimisation of enterprise talent structure faces challenges of high-dimensional and multiple constraints. Existing meta-heuristic methods mostly focus on a single objective and are unable to balance skills matching, generational balance, and strategic resilience. This paper proposes a person-job optimisation model based on particle swarm optimisation, which objectively assigns weights using the entropy weight method and introduces a penalty function to handle multi-objective conflicts. On a real human resource dataset from an enterprise, the area under the curve of person-job optimisation model based on particle swarm optimisation reached 0.94, which was 14.6% higher than that of traditional particle swarm optimisation; the talent-job matching degree increased from the initial 0.51 to 0.88, and the generational coupling coefficient reached 0.82. This model fills the gap in methods for the collaborative optimisation of multi-dimensional talent structures under the requirements of new quality productivity.

Keywords: new quality productive forces; optimisation of talent structure; particle swarm optimisation match degree.

DOI: 10.1504/IJICT.2026.156196

International Journal of Information and Communication Technology, 2026 Vol.27 No.97, pp.41 - 63

Received: 14 May 2026
Accepted: 19 Jun 2026
Published online: 07 Sep 2026 *

Title: Metaheuristic-based talent configuration optimisation for enterprises under the new quality productive forces framework

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