Open Access

ARTICLE

NTSSA: A Novel Multi-Strategy Enhanced Sparrow Search Algorithm with Northern Goshawk Optimization and Adaptive t-Distribution for Global Optimization

Hui Lv^1,#, Yuer Yang^2,3,4,#, Yifeng Lin^2,3,*

1 International Business College, Chengdu International Studies University, Chengdu, 611844, China
2 Department of Computer Science, The University of Hong Kong, Hong Kong, 999077, China
3 College of Cyber Security, Jinan University, Guangzhou, 511436, China
4 School of Economics, Jinan University, Guangzhou, 510632, China

* Corresponding Author: Yifeng Lin. Email:
# These authors contributed equally to this work

Computers, Materials & Continua 2025, 85(1), 925-953. https://doi.org/10.32604/cmc.2025.065709

Received 20 March 2025; Accepted 16 June 2025; Issue published 29 August 2025

Abstract

It is evident that complex optimization problems are becoming increasingly prominent, metaheuristic algorithms have demonstrated unique advantages in solving high-dimensional, nonlinear problems. However, the traditional Sparrow Search Algorithm (SSA) suffers from limited global search capability, insufficient population diversity, and slow convergence, which often leads to premature stagnation in local optima. Despite the proposal of various enhanced versions, the effective balancing of exploration and exploitation remains an unsolved challenge. To address the previously mentioned problems, this study proposes a multi-strategy collaborative improved SSA, which systematically integrates four complementary strategies: (1) the Northern Goshawk Optimization (NGO) mechanism enhances global exploration through guided prey-attacking dynamics; (2) an adaptive t-distribution mutation strategy balances the transition between exploration and exploitation via dynamic adjustment of the degrees of freedom; (3) a dual chaotic initialization method (Bernoulli and Sinusoidal maps) increases population diversity and distribution uniformity; and (4) an elite retention strategy maintains solution quality and prevents degradation during iterations. These strategies cooperate synergistically, forming a tightly coupled optimization framework that significantly improves search efficiency and robustness. Therefore, this paper names it NTSSA: A Novel Multi-Strategy Enhanced Sparrow Search Algorithm with Northern Goshawk Optimization and Adaptive t-Distribution for Global Optimization. Extensive experiments on the CEC2005 benchmark set demonstrate that NTSSA achieves theoretical optimal accuracy on unimodal functions and significantly enhances global optimum discovery for multimodal functions by 2–5 orders of magnitude. Compared with SSA, GWO, ISSA, and CSSOA, NTSSA improves solution accuracy by up to 14.3% (F8) and 99.8% (F12), while accelerating convergence by approximately 1.5–2×. The Wilcoxon rank-sum test (p < 0.05) indicates that NTSSA demonstrates a statistically substantial performance advantage. Theoretical analysis demonstrates that the collaborative synergy among adaptive mutation, chaos-based diversification, and elite preservation ensures both high convergence accuracy and global stability. This work bridges a key research gap in SSA by realizing a coordinated optimization mechanism between exploration and exploitation, offering a robust and efficient solution framework for complex high-dimensional problems in intelligent computation and engineering design.

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