Magnetotactic bacteria optimization algorithm based on best-rand scheme

2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014) Pub Date : 2014-10-16 DOI:10.1109/NaBIC.2014.6921854

Hongwei Mo, Mengjiao Geng

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引用次数: 8

Abstract

Magnetotactic bacteria optimization algorithm (MBOA) is a kind of optimization algorithm inspired by the characteristics of magnetotactic bacteria(MTB). They have chains consisting of micro magnetic particles called magnetosomes inside their bodies. These magnetic chains make MTB have magnetotaxis that make them orient and swim along geomagnetic field lines. The original MBOA mimics the interaction energy between magnetosomes chains to obtain moments for solving problems. But its performance is mainly update to operation of candidate solutions replacement with randomly generated cells. In this paper, an improved MBOA is proposed. It regulates the moments based on the information exchange between best individual's moments and some randomly one. It is called best-rand scheme. The performance of proposed algorithm is tested on twelve standard function problems and compared with some popular optimization algorithms, including variants of DE, ABC. Experiment results show that the improved algorithm is very effective in optimization problems and has superior performance to the compared methods on many benchmark functions.

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基于最优方案的趋磁细菌优化算法

趋磁细菌优化算法(MBOA)是一种受趋磁细菌特性启发的优化算法。它们体内有由称为磁小体的微磁性颗粒组成的链。这些磁链使MTB具有趋磁性，使它们沿着地磁场线定向和游动。原始的MBOA模拟磁小体链之间的相互作用能量来获得求解问题所需的力矩。但其性能主要更新为用随机生成的单元替换候选解的操作。本文提出了一种改进的MBOA。它基于最佳个体的时刻与随机个体的时刻之间的信息交换来调节时刻。它被称为最佳兰特方案。在12个标准函数问题上测试了该算法的性能，并与一些流行的优化算法(包括DE、ABC的变体)进行了比较。实验结果表明，改进后的算法在优化问题上是非常有效的，在许多基准函数上优于比较方法。

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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2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)

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