Article: Performance optimisation of a normalised operational assessment system using hybrid population intelligence algorithm Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.28 pp.33 - 48 Abstract: Aiming at the dynamic constraints and MOPs faced by the normalised operation appraisal system in industrial environments, this paper proposes a hybrid swarm intelligence framework that integrates PSO, GA and ACO. By constructing a hierarchical optimisation architecture - based on the synergistic mechanism of discrete decision-making layer, continuous parameter optimisation layer and variable operation layer a multimodal information fusion strategy and an adaptive parameter adjustment method are innovatively introduced. Experiments based on real industrial operation data show that compared with the traditional optimisation algorithms, the proposed method demonstrates improvements in response time and resource utilisation, with balanced multi-objective performance under dynamic constraints, especially in the dynamic constraint scenarios with stable convergence characteristics and robustness. The engineering deployment verifies the practical value of the framework in complex industrial systems and provides a new technical path for intelligent operation assessment. Inderscience Publishers - linking academia, business and industry through research

Title: Performance optimisation of a normalised operational assessment system using hybrid population intelligence algorithm

Authors: Jingfeng Qi; Pengfei Wang; Yaolong Li; Xiangyi Feng

Addresses: Department of Integrated Management, Yulin Information Operation and Maintenance Branch, SHCCIG Yubei Coal Industry Co., Ltd., Yulin 719000, China ' Department of Integrated Management, Yulin Information Operation and Maintenance Branch, SHCCIG Yubei Coal Industry Co., Ltd., Yulin 719000, China ' Department of Integrated Management, Yulin Information Operation and Maintenance Branch, SHCCIG Yubei Coal Industry Co., Ltd., Yulin 719000, China ' Department of Integrated Management, Yulin Information Operation and Maintenance Branch, SHCCIG Yubei Coal Industry Co., Ltd., Yulin 719000, China

Abstract: Aiming at the dynamic constraints and MOPs faced by the normalised operation appraisal system in industrial environments, this paper proposes a hybrid swarm intelligence framework that integrates PSO, GA and ACO. By constructing a hierarchical optimisation architecture - based on the synergistic mechanism of discrete decision-making layer, continuous parameter optimisation layer and variable operation layer a multimodal information fusion strategy and an adaptive parameter adjustment method are innovatively introduced. Experiments based on real industrial operation data show that compared with the traditional optimisation algorithms, the proposed method demonstrates improvements in response time and resource utilisation, with balanced multi-objective performance under dynamic constraints, especially in the dynamic constraint scenarios with stable convergence characteristics and robustness. The engineering deployment verifies the practical value of the framework in complex industrial systems and provides a new technical path for intelligent operation assessment.

Keywords: hybrid population intelligence algorithm; normalised operational appraisal system; multi-objective optimisation; dynamic constraint processing; adaptive mechanisms.

DOI: 10.1504/IJICT.2025.147708

International Journal of Information and Communication Technology, 2025 Vol.26 No.28, pp.33 - 48

Received: 20 May 2025
Accepted: 08 Jun 2025
Published online: 25 Jul 2025 *

Title: Performance optimisation of a normalised operational assessment system using hybrid population intelligence algorithm

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