Performance Study of a Bat Searching Algorithm From System Dynamics Perspective

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS

Mechatronic Systems and Control Pub Date : 2019-11-26 DOI:10.1115/dscc2019-9017

Haopeng Zhang, N. Schutte

引用次数: 0

Abstract

In this paper, the performance of a bat searching algorithm is studied from system dynamics point of view. Bat searching algorithm (BA) is a recently developed swarm intelligence based optimization algorithm which has shown great success when solving complicated optimization problems. Each bat in the BA has two main states: velocity and position. The position represents the solution of the optimization problems while the velocity represents the searching direction and step size during each iteration. Due to the nature of the update equations, the dynamics of the bats are formulated as a group of second-order discrete-time systems. In this paper, the performance of the algorithm is analyzed based on the nature of the responses in the second-order systems. The over-damped response, under-damped responses are studied and the parameters requirements are derived. Moreover, unstable scenarios of the bats are also considered when examining the performance of the algorithm. Numerical evaluations are conducted to test different choices of the parameters in the BA.

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基于系统动力学的蝙蝠搜索算法性能研究

本文从系统动力学的角度研究了蝙蝠搜索算法的性能。蝙蝠搜索算法(Bat searching algorithm, BA)是近年来发展起来的一种基于群体智能的优化算法，在解决复杂的优化问题方面取得了巨大的成功。BA中的每个蝙蝠都有两种主要状态:速度和位置。位置表示优化问题的解，速度表示每次迭代的搜索方向和步长。由于更新方程的性质，蝙蝠的动力学被表述为一组二阶离散时间系统。本文根据二阶系统响应的性质，分析了该算法的性能。研究了过阻尼响应和欠阻尼响应，并推导了参数要求。此外，在检验算法的性能时，还考虑了蝙蝠的不稳定情况。通过数值计算对BA中不同参数的选择进行了验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-

CiteScore

1.40

自引率

66.70%

发文量

期刊介绍： This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.