Document Type : Original Article

Author

• Aliakbar Tajari Siahmarzkooh

Assistant Professor, Department of Computer Sciences, Golestan University, Gorgan, Iran

Abstract

Inverse Heat Conduction Problems (IHCPs) are highly ill-posed, sensitive to measurement noise, and computationally demanding. This study proposes a hybrid SMA–PSO algorithm combining the exploratory strength of the Slime Mould Algorithm (SMA) with the fast convergence of Particle Swarm Optimization (PSO). In the proposed framework, SMA guides the early search to maintain diversity, while PSO progressively refines promising solutions, effectively balancing global exploration and local exploitation. The inverse problem is formulated as an unconstrained optimization task to reconstruct unknown boundary heat fluxes by minimizing discrepancies between simulated and observed temperatures. The algorithm is tested on standard 1D and 2D IHCP benchmarks under noise-free and noisy conditions (up to 7% Gaussian noise). Results from 30 independent runs show that SMA–PSO outperforms standalone SMA, PSO, and the GA–PSO hybrid. For a 2D case with 3% noise, SMA–PSO reduces RMSE by 27% versus PSO, 39% versus SMA, and accelerates convergence by 18% compared to GA–PSO. Paired t-tests confirm statistical significance. The method demonstrates strong noise robustness, stable convergence with low variance, and computational efficiency comparable to PSO. These findings highlight SMA–PSO as a reliable, efficient, and effective approach for solving complex IHCPs in engineering applications.

Keywords

• Inverse Heat Conduction Problem
• Slime Mould Algorithm
• Particle Swarm Optimization
• Hybrid Metaheuristic Optimization
• Ill-Posed Inverse Problems