Duc Truong Pham

THE BEES ALGORITHM, A NOVEL TOOL FOR COMPLEX OPTIMISATION PROBLEMS

Publisher Summary This chapter presents a new population-based search algorithm called the Bees Algorithm (BA). This algorithm mimics the food foraging behavior of swarms of honeybees. In its basic version, the algorithm performs a kind of neighborhood search combined with random search and can be used for both combinatorial optimization and functional optimization. This chapter focuses on the latter. Following a description of the algorithm, the chapter presents the results obtained for a number of benchmark problems demonstrating the efficiency and robustness of the new algorithm. The results show that the algorithm can reliably handle complex multi-model optimization problems without being trapped at local solutions. One of the drawbacks of the algorithm is the number of tunable parameters used. However, it is possible to set the parameter values by conducting a small number of trials.

A comparison of rapid prototyping technologies

Until recently, prototypes had to be constructed by skilled model makers from 2D engineering drawings. This is a time-consuming and expensive process. With the advent of new layer manufacturing and CAD/CAM technologies, prototypes may now be rapidly produced from 3D computer models. There are many different rapid prototyping (RP) technologies available. This paper presents an overview of the current technologies and comments on their strengths and weaknesses. Data are given for common process parameters such as layer thickness, system accuracy and speed of operation. A taxonomy is also suggested, along with a preliminary guide to process selection based on the end use of the prototype.

Selection of K in K-means clustering

Abstract The K-means algorithm is a popular data-clustering algorithm. However, one of its drawbacks is the requirement for the number of clusters, K, to be specified before the algorithm is applied. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Factors that affect this selection are then discussed and a new measure to assist the selection is proposed. The paper concludes with an analysis of the results of using the proposed measure to determine the number of clusters for the K-means algorithm for different data sets.

Control chart pattern recognition using learning vector quantization networks

Pattern recognition systems using neural networks for discriminating between different types of control chart patterns are discussed. A class of pattern recognizers based on the Learning Vector Quantization (LVQ) network is described. A procedure to increase the classification accuracy and decrease the learning time for LVQ networks is presented. The results of control chart pattern recognition experiments using both existing LVQ networks and an LVQ network implementing the proposed procedure are given.

Derivation of optimal walking motions for a bipedal walking robot

The problem of determining energy optimal walking motions for a bipedal walking robot is considered. A full dynamic model of a planar seven-link biped with feet is derived including the effects of impact of the feet with the ground. Motions of the hip and feet during a regular step are then modelled by 3rd order polynomials, the coefficients of which are obtained by numerically minimising an energy cost function. Results are given in the form of walking profiles and energy curves for the specific cases of motion over level ground, motion up and down an incline, and varying payload.

Optimising Neural Networks for Identification of Wood Defects Using the Bees Algorithm

This paper presents an application of the bees algorithm (BA) to the optimisation of neural networks for wood defect detection. This novel population-based search algorithm mimics the natural foraging behaviour of swarms of bees. In its basic version, the algorithm performs a kind of neighbourhood search combined with random search. Following a brief description of the algorithm, the paper gives the results obtained for the wood defect identification problem demonstrating the efficiency and robustness of the new algorithm.

Feature-based control chart pattern recognition

This paper describes a new approach for the recognition of control chart patterns (CCPs). The approach uses features extracted from a CCP instead of the unprocessed CCP data or its statistical properties for the recognition task. These features represent the shape of the CCP explicitly. The approach has two main steps: (1) extraction of features and (2) recognition of patterns. A set of CCP feature extraction procedures are described in the paper. The extracted features are recognized using heuristics, induction and neural network techniques. The paper presents the results of analysing several hundred control chart patterns and gives a comparison with those reported in previous work.

The Bees Algorithm: modelling foraging behaviour to solve continuous optimization problems

Abstract The Bees Algorithm models the foraging behaviour of honeybees in order to solve optimization problems. The algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. This article describes the Bees Algorithm in its basic formulation, and two recently introduced procedures that increase the speed and accuracy of the search. A critical review of the related swarm intelligence literature is presented. The effectiveness of the proposed method is compared to that of three state-of-the-art biologically inspired search methods. The four algorithms were tested on a range of well-known benchmark function optimization problems of different degrees of complexity. The experimental results proved the reliability of the bees foraging metaphor. The Bees Algorithm performed optimally, or near optimally, in almost all the tests. Compared to the three control algorithms, the Bees Algorithm was highly competitive in terms of learning accuracy and speed. The experimental tests helped also to shed further light on the search mechanisms of the Bees Algorithm and the three control methods, and to highlight their differences, strengths, and weaknesses.

Application of the Bees Algorithm to the Training of Learning Vector Quantisation Networks for Control Chart Pattern Recognition

Control charts are employed in manufacturing industry for statistical process control (SPC). It is possible to detect incipient problems and prevent a process from going out of control by identifying the type of patterns displayed by the control charts. Various techniques have been applied to this control chart pattern recognition task. This paper presents the use of learning vector quantisation (LVQ) networks for recognising patterns in control charts. The LVQ networks were trained, not by applying standard training algorithms, but by employing a new optimisation algorithm developed by the authors. The algorithm, called the bees algorithm, is inspired by the food foraging behaviour of honey bees. The paper first describes the bees algorithm and explains how the algorithm is employed to train LVQ networks. It then discusses the recognition of control chart patterns by LVQ networks optimised using the bees algorithm

Control chart pattern recognition using a new type of self-organizing neural network

Abstract Control charts as used in statistical process control can exhibit six principal types of patterns: normal, cyclic, increasing trend, decreasing trend, upward shift and downward shift. Apart from normal patterns, all the other patterns indicate abnormalities in the process that must be corrected. Accurate and speedy detection of such patterns is important to achieving tight control of the process and ensuring good product quality. This paper describes a new type of neural network for control chart pattern recognition. The neural network is self-organizing and can learn to recognize new patterns in an on-line incremental manner. The key feature of the proposed neural network is the criterion employed to select the firing neuron, i.e. the neuron indicating the pattern class. The paper gives a comparison of the results obtained using the proposed network and those for other self-organizing networks employing a different firing criterion.