Optimisation of Engineering Systems With the Bees Algorithm

Optimisation of Engineering Systems With the Bees Algorithm

Duc T. Pham (University of Birmingham, Birmingham, UK), Luca Baronti (University of Birmingham, Birmingham, UK), Biao Zhang (Harbin Institute of Technology, Harbin, China) and Marco Castellani (University of Birmingham, Birmingham, UK)
Copyright: © 2018 |Pages: 15
DOI: 10.4018/IJALR.2018010101


This article describes the Bees Algorithm in standard formulation and presents two applications to real-world continuous optimisation engineering problems. In the first case, the Bees Algorithm is employed to train three artificial neural networks (ANNs) to model the inverse kinematics of the joints of a three-link manipulator. In the second case, the Bees Algorithm is used to optimise the parameters of a linear model used to approximate the torque output for an electro-hydraulic load system. In both cases, the Bees Algorithm outperformed the state-of-the-art in the literature, proving to be an effective optimisation technique for engineering systems.
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2. The Bees Algorithm

The Bees Algorithm is a nature-inspired optimisation method based on the foraging behaviour of honey bees. A number of agents (scout bees) are used to explore randomly the solution space, looking for regions of high fitness. The regions (sites) of highest fitness are further searched by forager bees which carry out local exploitative search. The Bees Algorithm repeats cycles of global (random) and local search until an acceptable solution is discovered, or a given number of iterations have elapsed.

The Bees Algorithm makes no assumption on the nature of the solution space, such as its derivability or continuity (Pham and Castellani, 2009b). For this reason, it is applicable to a wide range of continuous and combinatorial problems. Henceforth, unless explicitly stated, continuous optimisation problems will be considered.

Many variants of the BA have been developed, with variations in the way the bees are recruited, when and how a site is abandoned, and other key aspects of the algorithm. A recent survey (Hussein, Sahran, and Abdullah, 2017) suggests a classification of the Bees Algorithm in three different branches: one referred to as the Basic Bees Algorithm (BBA) by several authors (Pham, Castellani, and Fahmy, 2008; Packianather, Landy, and Pham, 2009; Pham and Castellani, 2009b; Yuce et al., 2013), where different sites are searched using a fixed search scope radius; one also considering a reduction (shrinking) of the scope of the local search shrinking-based BA (ShBA) (Pham, Ghanbarzadeh, et al., 2006a), and one regarded as the Standard BA (SBA) (Castellani, Pham, and Pham, 2012; Pham, Pham, and Castellani, 2012), including neighbourhood shrinking and site abandonment.

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