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The Bees Algorithm Theory, Improvements and Applications The Bees Algorithm Theory, Improvements and Applications A thesis submitted to Cardiff University For the degree of Doctor of Philosophy by Ebubekir Ko$ Manufacturing Engineering Centre School of Engineering University of Wales, Cardiff United Kingdom March 2010 UMI Number: U585416 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. Dissertation Publishing UMI U585416 Published by ProQuest LLC 2013. Copyright in the Dissertation held by the Author. Microform Edition © ProQuest LLC. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code. ProQuest LLC 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, Ml 48106-1346 Abstract In this thesis, a new population-based search algorithm called the Bees Algorithm (BA) is presented. The algorithm mimics the food foraging behaviour of swarms of honey bees. In its basic version, the algorithm performs a kind of neighbourhood search combined with random search and can be used for both combinatorial and functional optimisation. In the context of this thesis both domains are considered. Following a description of the algorithm, the thesis gives the results obtained for a number of complex problems demonstrating the efficiency and robustness of the new algorithm. Enhancements of the Bees Algorithm are also presented. Several additional features are considered to improve the efficiency of the algorithm. Dynamic recruitment, proportional shrinking and site abandonment strategies are presented. An additional feature is an evaluation of several different functions and of the performance of the algorithm compared with some other well-known algorithms, including genetic algorithms and simulated annealing. The Bees Algorithm can be applied to many complex optimisations problems including multi-layer perceptrons, neural networks training for statistical process control and the identification of wood defects in wood veneer sheets. Also, the algorithm can be used to design 2D electronic recursive filters, to show its potential in electronics applications. A new structure is proposed so that the algorithm can work in combinatorial domains. In addition, several applications are presented to show the robustness of the algorithm in various conditions. Also, some minor modifications are proposed for representations of the problems since it was originally developed for continuous domains. In the final part, a new algorithm is introduced as a successor to the original algorithm. A new neighbourhood structure called Gaussian patch is proposed to reduce the complexity of the algorithm as well as increasing its efficiency. The performance of the algorithm is tested by use on several multi-model complex optimisation problems and this is compared to the performance of some well-known algorithms. To my wife and daughter Acknowledgements I would like to express my sincere gratitude to my supervisor Professor D.T. Pham for his excellent guidance throughout my research and his huge support during the more difficult stages of my research. I would also like to thank my family for the support and encouragement they have given me always, especially my wife and my beautiful daughter for their immense patience. Special thanks to Professor Sakir Kocabas who introduced me to the field of artificial intelligence several years ago and Professor Ercan Oztemel who helped me to meet Professor D.T. Pham. Thank you to the members of “BayBees” group for providing interesting comments and presentations in our daily and weekly meetings. Finally I would like to thank the ORS award, the MEC and institutions from Turkey for funding my research. DECLARATION AND STATEMENTS DECLARATION This work has not previously been accepted in substance for any degree and is not concurrently submitted in candidature for any degree. Signed<ff!^K ^^'^/rrrrrXEbubekir K 0 9 ) Date 05/02/2010 STATEMENT 1 This thesis is being submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy (PhD). Signed \?<r<fSrr. .T.. 7(Ebubekir K 0 9 ) Date 05/02/2010 STATEMENT 2 This thesis is the result of my own independent work/investigation, except where otherwise stated. Other sources are acknowledged by explicit references. Signed . ."tEbubekir K 0 9 ) Date 05/02/2010 STATEMENT 3 I hereby give consent for my thesis, if accepted, to be available for photocopying and for inter-library loan, and for the title and summary to be made available to outside organisations. Signed TTT. (Ebubekir Ko?) Date 05/02/2010 Contents Abstract i Acknowledgements iv Declaration v Contents vi List of Figures x List of Tables xiii Abbreviations xiv List of Symbols xvi 1 INTRODUCTION...............................................................................................................1 1.1. M o t iv a t io n .................................................................................................................................................. 1 1.2. A im s a n d O b j e c t iv e s ............................................................................................................................. 3 1.3. M e t h o d s ........................................................................................................................................................ 3 1.4. O u t l in e o f t h e T h e s is .......................................................................................................................... 4 2 BACKGROUND..................................................................................................................7 2 .1 . S w a r m Intelligence ............................................................................................................................ 7 2 .2 . In t e l l ig e n t S w a r m - b a s e d O ptimisation ..............................................................................9 2.2.1. Evolutionary algorithms .................................................................................. 10 2.2.2. Ant colony optimisation .................................................................................. 13 2.2.3. Particle swarm optimisation ............................................................................17 2 .3 . B e e s in N a t u r e : F o o d F o r a g in g a n d N e s t S ite S e l e c t io n B e h a v io u r s 21 2 .4 . C omputational simulations o f h o n e y b e e b e h a v io u r s ...........................................23 2 .4 .1. Nectar-Source Selection Models ...................................................................................... 2 4 2.4.2. Nest-Site Selection Models .............................................................................30 2.5. H o n e y - b e e s in s p ir e d a l g o r it h m s .............................................................................................31 2.6. S u m m a r y .....................................................................................................................................................47 3 THE BEES ALGORITHM: THEORY AND IMPROVEMENTS......................... 48 3.1. P reliminaries ........................................................................................................................................... 48 3.2. T h e B e e s A l g o r it h m ........................................................................................................................... 50 3.3. C haracteristics o f t h e p r o p o s e d B e e s A l g o r it h m .................................................... 54 3.3.1. Neighbourhood Search....................................................................................54 3.3.2. Site Selection ....................................................................................................57 3.3.3. Global Search ...................................................................................................57 3.4. Improvements o n l o c a l a n d g l o b a l s e a r c h ................................................................... 59 3.4.1. Dynamic neighbourhood ................................................................................ 59 3.4.2. Proportional shrinking .....................................................................................61 3.4.3. Site abandonment ............................................................................................. 62 3.5. E xperimental R e s u l t s ......................................................................................................................64 3 .6 . S u m m a r y .....................................................................................................................................................77 4 BEES ALGORITHM FOR CONTINUOUS DOMAINS......................................... 78 4 .1 . P reliminaries ..........................................................................................................................................78 4.2. O ptimisation o f th e W e ig h t s o f M u l t i -L a y e r e d P e r c e p t r o n s U sin g t h e B e e s A l g o r it h m f o r P a t t e r n R e c o g n it io n in S t a t is t ic a l P r o c e s s C o n t r o l C h a r t s ........................................................................... 79 4.2.1. Control
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