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首页> 外文期刊>Arabian Journal for Science and Engineering >Accelerated Opposition-Based Antlion Optimizer with Application to Order Reduction of Linear Time-Invariant Systems
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Accelerated Opposition-Based Antlion Optimizer with Application to Order Reduction of Linear Time-Invariant Systems

机译：基于加速的基于对抗的蚁群优化器在线性时不变系统的降阶中的应用

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摘要

This paper proposes a novel variant of antlion optimizer (ALO), namely accelerated opposition-based antlion optimizer (OB-ac-ALO). This modified version is conceptualized with opposition-based learning (OBL) model and integrated with acceleration coefficient (ac). The OBL model approximates the original as well as opposite candidate solutions simultaneously during evolution process. The implementation of OBL technique is collaborated with an exploitation acceleration coefficient which is useful in local searching and tends to find global optimum efficiently. A position update equation is formulated using these strategies. To validate the proposed technique, a broad set of 21 benchmark test suit of extensive variety of features is chosen. To analyse the performance of proposed algorithm, various analysis metrics such as search history, trajectories and average distance between search agents before and after improving the algorithm are performed. A nonparametric Wilcoxon ranksum test is applied to show its statistical significance. It is applied to solve a real-world application for approximating the higher-order linear time-invariant system to its corresponding lower-order invariant system. Three single-input single-output problems have been considered in terms of integral square error.
机译：本文提出了一种新型的蚁群优化器（ALO），即基于加速对冲的蚁群优化器（OB-ac-ALO）。此修改版本使用基于对立的学习（OBL）模型进行概念化，并与加速度系数（ac）集成在一起。 OBL模型在演化过程中同时逼近原始和相反的候选解。 OBL技术的实现与开发加速系数配合使用，该开发加速系数在局部搜索中很有用，并且倾向于有效地找到全局最优值。使用这些策略制定位置更新方程式。为了验证所提出的技术，选择了广泛的21种具有多种功能的基准测试套件。为了分析所提出算法的性能，在改进算法之前和之后，执行了各种分析指标，例如搜索历史，轨迹和搜索代理之间的平均距离。应用非参数Wilcoxon ranksum检验来显示其统计意义。它用于解决将高阶线性时不变系统近似为其对应的低阶不变系统的现实应用。就积分平方误差而言，已经考虑了三个单输入单输出问题。

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