Multi-strategy barrel theory-based optimizer for global optimization and engineering problems

Abstract Although the barrel theory-based optimizer (BTO) presents a novel framework for optimization, it frequently encounters challenges such as stagnant population diversity, insufficient global exploration, and a propensity for premature convergence. To address these issues, this paper proposes an improved barrel theory-based optimizer (IBTO) algorithm incorporating three strategic enhancements: Tent chaotic initialization for enhanced population distribution, an improved differential evolution strategy to strengthen global search, and an adaptive Gaussian–Lévy cyclic perturbation mechanism to facilitate the ability to escape local optima. High-performance computing is necessary to support the resulting computational intensity. The performance of IBTO is evaluated on the CEC2017 and CEC2022 benchmark suites as well as five constrained engineering design problems. Experimental results demonstrate that IBTO achieves superior competitiveness and favorable statistical performance, especially in high-dimensional tasks. Furthermore, the results on engineering design cases underscore the robustness and significant practical potential of the proposed method. Similar content being viewed by others Data availability No datasets were generated or analyzed during the current study. References Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Transact Syst Sci Cybernetics 4(2):100–107. https://doi.org/10.1109/tssc.1968.300136 Khatib O (1986) Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Res 5(1):90–98. https://doi.org/10.1177/027836498600500106 LaValle S (1998) Rapidly-exploring random trees: a new tool for path planning. Research Report 9811 Zhao Z, Liu R (2015) A optimization of a* algorithm to make it close to human pathfinding behavior. 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Ye Lin, and Chuhang Chen oversaw the project administration and secured funding. All authors read and approved the final manuscript. Corresponding author Ethics declarations Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Additional information Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Cite this article Jiang, Y., Su, D., Li, J. et al. Multi-strategy barrel theory-based optimizer for global optimization and engineering problems. J Supercomput 82, 664 (2026). https://doi.org/10.1007/s11227-026-08800-2 Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1007/s11227-026-08800-2