Nature-Inspired Intelligent Optimisation Using the Bees Algorithm

2014 ◽  
pp. 38-69 ◽  
Author(s):  
Duc Truong Pham ◽  
Marco Castellani ◽  
Hoai An Le Thi
Keyword(s):  
Bees Algorithm ◽  
Intelligent Optimisation

scholarly journals Machine learning of electro-hydraulic motor dynamics

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Luca Baronti ◽  
Biao Zhang ◽  
Marco Castellani ◽  
Duc Truong Pham
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Analytical Model ◽  
Random Search ◽  
Bees Algorithm ◽  
Multi Layer Perceptron ◽  
Hydraulic Motor ◽  
Neural Network Models ◽  
Load Simulator ◽  
Intelligent Optimisation

AbstractIn this paper we propose an innovative machine learning approach to the hydraulic motor load balancing problem involving intelligent optimisation and neural networks. Two different nonlinear artificial neural network approaches are investigated, and their accuracy is compared to that of a linearised analytical model. The first neural network approach uses a multi-layer perceptron to reproduce the load simulator dynamics. The multi-layer perceptron is trained using the Rprop algorithm. The second approach uses a hybrid scheme featuring an analytical model to represent the main system behaviour, and a multi-layer perceptron to reproduce unmodelled nonlinear terms. Four techniques are tested for the optimisation of the parameters of the analytical model: random search, an evolutionary algorithm, particle swarm optimisation, and the Bees Algorithm. Experimental tests on 4500 real data samples from an electro-hydraulic load simulator rig reveal that the accuracy of the hybrid and the neural network models is comparable, and significantly superior to the accuracy of the analytical model. The results of the optimisation procedures suggest also that the inferior performance of the analytical model is likely due to the non-negligible magnitude of the unmodelled nonlinearities, rather than suboptimal setting of the parameters. Despite its limitations, the analytical linear model performs comparably to the state-of-the-art in the literature, whilst the neural and hybrid approaches compare favourably.

Fuzzy logic, neural-fuzzy network and honey bees algorithm to develop the swarm motion of aerial robots

2021 ◽  
Author(s):  
Iman Shafieenejad ◽  
Elham Dehghan Rouzi ◽  
Jamshid Sardari ◽  
Mohammad Siami Araghi ◽  
Amirhosein Esmaeili ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Honey Bees ◽  
Bees Algorithm ◽  
Aerial Robots ◽  
Fuzzy Network ◽  
Neural Fuzzy Network ◽  
Neural Fuzzy

Engineering Problem Optimized Using Bees Algorithm

2019 ◽  
pp. 99-105
Author(s):  
Kaushik Kumar ◽  
Divya Zindani ◽  
J. Paulo Davim
Keyword(s):  
Engineering Problem ◽  
Bees Algorithm

scholarly journals A Hybrid SFL-Bees Algorithm

2015 ◽  
Vol 128 (5) ◽  
pp. 13-18
Author(s):  
Duc Hoang
Keyword(s):  
Bees Algorithm

scholarly journals Bees Algorithm for multimodal function optimisation

2015 ◽  
Vol 230 (5) ◽  
pp. 867-884 ◽  
Author(s):  
ZD Zhou ◽  
YQ Xie ◽  
DT Pham ◽  
S Kamsani ◽  
M Castellani
Keyword(s):  
Global Optimum ◽  
Bees Algorithm ◽  
Benchmark Problems ◽  
Search Technique ◽  
Local Optima ◽  
Multimodal Function ◽  
Real World Applications ◽  
Speed Up ◽  
Search Approach ◽  
Optimisation Procedure

The aim of multimodal optimisation is to find significant optima of a multimodal objective function including its global optimum. Many real-world applications are multimodal optimisation problems requiring multiple optimal solutions. The Bees Algorithm is a global optimisation procedure inspired by the foraging behaviour of honeybees. In this paper, several procedures are introduced to enhance the algorithm’s capability to find multiple optima in multimodal optimisation problems. In the proposed Bees Algorithm for multimodal optimisation, dynamic colony size is permitted to automatically adapt the search effort to different objective functions. A local search approach called balanced search technique is also proposed to speed up the algorithm. In addition, two procedures of radius estimation and optima elitism are added, to respectively enhance the Bees Algorithm’s ability to locate unevenly distributed optima, and eliminate insignificant local optima. The performance of the modified Bees Algorithm is evaluated on well-known benchmark problems, and the results are compared with those obtained by several other state-of-the-art algorithms. The results indicate that the proposed algorithm inherits excellent properties from the standard Bees Algorithm, obtaining notable efficiency for solving multimodal optimisation problems due to the introduced modifications.

Novel hybrid fuzzy-Bees algorithm for optimal feeder multi-objective reconfiguration by considering multiple-distributed generation

Energy ◽  
2014 ◽  
Vol 71 ◽  
pp. 507-515 ◽  
Author(s):  
Hajar Bagheri Tolabi ◽  
Mohd Hasan Ali ◽  
Shahrin Bin Md Ayob ◽  
M. Rizwan
Keyword(s):  
Distributed Generation ◽  
Bees Algorithm ◽  
Multi Objective

A New Initialization Algorithm for Bees Algorithm

2013 ◽  
pp. 39-52 ◽  
Author(s):  
Wasim A. Hussein ◽  
Shahnorbanun Sahran ◽  
Siti Norul Huda Sheikh Abdullah
Keyword(s):  
Bees Algorithm ◽  
Initialization Algorithm
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