Akademik Veri Yönetim Sistemi
ADVANCED ENGINEERING INFORMATICS, cilt.71, sa.Part B, ss.104298, 2026 (SCI-Expanded, Scopus)
This study proposes an improved tuna swarm optimization algorithm (I-TSO) for solving global optimization and engineering design problems. However, despite its strong global search ability, tuna swarm optimization (TSO) suffers from trapping in local optima, having premature convergence, and the loss of diversity in the early stage. To eliminate these disadvantages and improve the original TSO, the proposed I-TSO algorithm uses a dimension learning-based hunting (DLH) strategy. DLH constructs a neighborhood for each tuna in the population and uses that information in the optimization process. Thus, it improves population diversity, provides a proper balance between exploration and exploitation, and prevents trapping into local optima. The performance of the proposed algorithm is evaluated on 23 classical benchmark functions, CEC-2017, CEC-2020, and CEC-2022 test suites, and compared it with eight other optimization algorithms. Comparative results demonstrate that I-TSO exhibits stable and effective optimization capabilities. Further, the Friedman test and Wilcoxon signed-rank test are conducted to statistically evaluate the performance of the proposed algorithm, and thus its superiority is statistically confirmed. Moreover, the applicability of the I-TSO in real-world problems is validated on eight engineering design problems. Consequently, the I-TSO algorithm is capable of solving both numerical and engineering design problems with its efficient and superior performance.