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Adaptive Central Force Optimization Algorithm Based on the Stability Analysis

机译:基于稳定性分析的自适应中心力优化算法

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摘要

In order to enhance the convergence capability of the central force optimization (CFO) algorithm, an adaptive central force optimization (ACFO) algorithm is presented by introducing an adaptive weight and defining an adaptive gravitational constant. The adaptive weight and gravitational constant are selected based on the stability theory of discrete time-varying dynamic systems. The convergence capability of ACFO algorithm is compared with the other improved CFO algorithm and evolutionary-based algorithm using 23 unimodal and multimodal benchmark functions. Experiments results show that ACFO substantially enhances the performance of CFO in terms of global optimality and solution accuracy.
机译:为了增强中心力优化算法的收敛能力,通过引入自适应权重并定义自适应重力常数,提出了一种自适应中心力优化算法。根据离散时变动力系统的稳定性理论选择自适应权重和重力常数。使用23个单峰和多峰基准函数,将ACFO算法的收敛能力与其他改进的CFO算法和基于进化的算法进行了比较。实验结果表明,ACFO在全局最优性和解决方案准确性方面大大提高了CFO的性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第9期|914789.1-914789.10|共10页
  • 作者单位

    Bohai Univ, Coll Math & Phys, Jinzhou 121000, Peoples R China.;

    Bohai Univ, Coll Math & Phys, Jinzhou 121000, Peoples R China.;

    Bohai Univ, Coll Informat Sci & Technol, Jinzhou 121000, Peoples R China.;

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