Scholarlink

<p>This paper introduces a novel metaheuristic algorithm, the Buck Converter Dynamics Optimizer (BCDO), for solving complex global optimization problems. Diverging from traditional nature-inspired algorithms, BCDO draws its core metaphor from the physical dynamics and closed-loop control of a DC–DC buck converter. The algorithm models a population of search agents as converter state-space vectors, whose behavior is governed by an embedded Proportional-Integral-Derivative (PID) controller. The “fitness error" of an agent is used to compute a “duty cycle," which adaptively balances the algorithm’s exploration and exploitation phases. This physics-based mechanism allows for an intelligent, self-tuning search strategy. The performance of BCDO is rigorously evaluated against a suite of standard Congress on Evolutionary Computation (CEC) 2017 benchmark functions and compared with several state-of-the-art metaheuristics. Furthermore, BCDO is applied to a challenging power electronics design problem: the optimal tuning of a PID controller for the same buck converter under dynamic-line and reference-tracking conditions. Simulation results demonstrate that BCDO is highly competitive, offering superior convergence speed and solution accuracy, particularly in the engineering case study.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Buck converter dynamics optimizer (BCDO): a novel control-inspired metaheuristic for global optimization

  • 期刊论文

  • 发表日期：

    2026年6月1日

查看全文
  • Muzammil Ahmed,
  • Shyamantak Raj Barman

摘要

This paper introduces a novel metaheuristic algorithm, the Buck Converter Dynamics Optimizer (BCDO), for solving complex global optimization problems. Diverging from traditional nature-inspired algorithms, BCDO draws its core metaphor from the physical dynamics and closed-loop control of a DC–DC buck converter. The algorithm models a population of search agents as converter state-space vectors, whose behavior is governed by an embedded Proportional-Integral-Derivative (PID) controller. The “fitness error" of an agent is used to compute a “duty cycle," which adaptively balances the algorithm’s exploration and exploitation phases. This physics-based mechanism allows for an intelligent, self-tuning search strategy. The performance of BCDO is rigorously evaluated against a suite of standard Congress on Evolutionary Computation (CEC) 2017 benchmark functions and compared with several state-of-the-art metaheuristics. Furthermore, BCDO is applied to a challenging power electronics design problem: the optimal tuning of a PID controller for the same buck converter under dynamic-line and reference-tracking conditions. Simulation results demonstrate that BCDO is highly competitive, offering superior convergence speed and solution accuracy, particularly in the engineering case study.