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Multi-strategy barrel theory-based optimizer for global optimization and engineering problems
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DOI：10.1007/s11227-026-08800-2.png)
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Abstract
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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.
Keywords:
Barrel theory-based optimizer
Tent chaotic initialization
Improved differential evolution
Adaptive Gaussian–Lévy cyclic perturbation
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