Zhe Liu^1,2,3,*, Sijia Zhu⁴, Yulong Huang^1,*, Tapan Senapati^5,6,7, Xiangyu Li⁸, Wulfran Fendzi Mbasso⁹, Himanshu Dhumras¹⁰, Mehdi Hosseinzadeh^11,12,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2157-2188, 2025, DOI:10.32604/cmes.2025.072352 - 26 November 2025

Abstract Uncertainty and ambiguity are pervasive in real-world intelligent systems, necessitating advanced mathematical frameworks for effective modeling and analysis. Fermatean fuzzy sets (FFSs), as a recent extension of classical fuzzy theory, provide enhanced flexibility for representing complex uncertainty. In this paper, we propose a unified parametric divergence operator for FFSs, which comprehensively captures the interplay among membership, non-membership, and hesitation degrees. The proposed operator is rigorously analyzed with respect to key mathematical properties, including non-negativity, non-degeneracy, and symmetry. Notably, several well-known divergence operators, such as Jensen-Shannon divergence, Hellinger distance, and χ²-divergence, are shown to be special cases More >

Tengda Li, Gang Wang, Qiang Fu^*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2559-2586, 2024, DOI:10.32604/cmes.2024.052039 - 08 July 2024

Abstract Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation (DTA) and high-dimensional decision space with single agent, this paper combines the deep reinforcement learning (DRL) theory and an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG-D2) algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA. The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, and considers the introduction of a double noise mechanism to increase the action exploration… More >

Tengda Li, Gang Wang, Qiang Fu^*, Xiangke Guo, Minrui Zhao, Xiangyu Liu

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3499-3522, 2023, DOI:10.32604/cmc.2023.041253 - 08 October 2023

Abstract Aiming at the problems of traditional dynamic weapon-target assignment algorithms in command decision-making, such as large computational amount, slow solution speed, and low calculation accuracy, combined with deep reinforcement learning theory, an improved Deep Deterministic Policy Gradient algorithm with dual noise and prioritized experience replay is proposed, which uses a double noise mechanism to expand the search range of the action, and introduces a priority experience playback mechanism to effectively achieve data utilization. Finally, the algorithm is simulated and validated on the ground-to-air countermeasures digital battlefield. The results of the experiment show that, under the… More >