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Cuckoo Search and firefly algorithm.pdf
Preface
Contents
Contributors
1 Cuckoo Search and Firefly Algorithm: Overview and Analysis
1 Introduction
2 Optimization Problems
3 The Essence of an Optimization Algorithm
3.1 The Essence of an Algorithm
3.2 An Ideal Algorithm?
3.3 Metaheuristic Algorithms
4 Cuckoo Search and Analysis
4.1 Cuckoo Search
4.2 Special Cases of Cuckoo Search
4.3 Why Cuckoo Search is so Efficient?
4.4 Global Convergence: Brief Mathematical Analysis
4.5 Applications
5 Firefly Algorithm and Analysis
5.1 Firefly Algorithm
5.2 Parameter Settings
5.3 Algorithm Complexity
5.4 Special Cases of FA
5.5 Variants of Firefly Algorithm
5.6 Attraction and Diffusion
5.7 Why SA is Efficient
5.8 Applications
6 Right Amount of Randomization
6.1 How to do Random Walks
6.2 Accuracy and Number of Iterations
7 Parameter Tuning and Parameter Control
7.1 Parameter Tuning
7.2 Parameter Control
8 Discussions and Concluding Remarks
References
2 On the Randomized Firefly Algorithm
1 Introduction
2 Background Information
2.1 Uniform Distribution
2.2 Normal or Gaussian Distribution
2.3 Lévy Flights
2.4 Chaotic Maps
2.5 Random Sampling in Turbulent Fractal Cloud
3 Randomized Firefly Algorithms
3.1 Original Firefly Algorithm
3.2 Variants of the Randomized Firefly Algorithm
4 Experiments and Results
4.1 Test Suite
4.2 Experimental Setup
4.3 PC Configuration
4.4 Results
5 Conclusion
References
3 Cuckoo Search: A Brief Literature Review
1 Introduction
2 Cuckoo Search: Variants and Hybrids
2.1 Variants
2.2 Hybrid Algorithms
2.3 Multi-objective Optimization
3 Engineering Optimization
4 Applications
5 Theoretical Analysis and Implementation
5.1 Theory and Algorithm Analysis
5.2 Improvements and Other Studies
5.3 Implementations
6 Conclusion
References
4 Improved and Discrete Cuckoo Search for Solving the Travelling Salesman Problem
1 Introduction
2 Travelling Salesman Problem
2.1 Description of TSP
2.2 Approximate Approaches to Solve TSP
3 Cuckoo Search and Discrete Cuckoo Search
3.1 Basic CS
3.2 Improved CS
3.3 Discrete Cuckoo Search for Travelling Salesman Problem
4 Experimental Results
5 Conclusion
References
5 Comparative Analysis of the Cuckoo Search Algorithm
1 Introduction
2 Comparison Algorithms
2.1 Differential Evolution (DE)
2.2 Particle Swarm Optimization (PSO)
2.3 Artificial Bee Colony (ABC)
3 Structural Analysis of Cuckoo Search Algorithm (CS)
4 Experiments
4.1 Control Parameters of the Comparison Algorithms
4.2 Statistical Tests
4.3 Test Functions
4.4 Algorithmic Precision
4.5 Statistical Results of Tests
5 Conclusions
References
6 Cuckoo Search and Firefly Algorithm Applied to Multilevel Image Thresholding
1 Introduction
2 Multilevel Thresholding Problem Formulation
2.1 Entropy Criterion Method
2.2 Between-class Variance Method
3 Proposed CS Approach to Multilevel Thresholding
4 Proposed FA Approach to Multilevel Threshlding
5 Experimental Study
5.1 Parameter Settings
5.2 Solution Quality Analysis
5.3 Computational Time Analysis
6 Conclusion
References
7 A Binary Cuckoo Search and Its Application for Feature Selection
1 Introduction
2 Supervised Classification Through Optimum-Path Forest
3 Cuckoo Search
3.1 Standard Cuckoo Search
3.2 Binary Cuckoo Search for Feature Selection
4 Methodology
5 Simulation, Results and Discussion
6 Conclusions
References
8 How to Generate the Input Current for Exciting a Spiking Neural Model Using the Cuckoo Search Algorithm
1 Introduction
2 Cuckoo Search Algorithm
3 Spiking Neural Models
4 Proposed Methodology
4.1 Approach for Designing the Input Current Equation
5 Experimental Results
5.1 Analysis and Comparisson of Experimental Results
5.2 Application of the Proposed Methodology in a Crop Classification Problem
5.3 Application of the Proposed Methodology in a Odor Recognition Problem
6 Conclusions
References
9 Multi-Objective Optimization of a Real-World Manufacturing Process Using Cuckoo Search
1 Introduction
2 Real-World Manufacturing Optimization
3 Cuckoo Search
3.1 Algorithm Description
3.2 Multi-Objective Extension
4 Evaluation
4.1 Configuration
4.2 Integrating the Optimization Algorithm and the Simulation
4.3 User Interface
4.4 Results
5 Analysis
5.1 Technical Analysis
5.2 User Perspective
6 Conclusions
References
10 Solving Reliability Optimization Problems by Cuckoo Search
1 Introduction
2 Cuckoo Search Algorithm
2.1 Cuckoo Breeding Behavior
2.2 Cuckoo Search
3 Case Studies: Reliability Optimization Problems
3.1 Case Study 1: A Complex (Bridge) System
3.2 Case study 2: A Series System
3.3 Case study 3: A Series-parallel System
3.4 Case study 4: An Overspeed System for a Gas Turbine
3.5 Case study 5: Large-Scale System Reliability Problem
3.6 Case study 6: 10-Unit System Reliability Problem with Different Combinations of Parameters
3.7 Case study 7: 15-Unit System Reliability Problem with Different Combinations of Parameters
4 Experimental Results, Analysis and Discussion
5 Conclusion
References
11 Hybridization of Cuckoo Search and Firefly Algorithms for Selecting the Optimal Solution in Semantic Web Service Composition
1 Introduction
2 Background
2.1 Nature-Inspired Metaheuristics
2.2 Hybridization of Nature-Inspired Metaheuristics
2.3 Semantic Web Service Composition Flow
3 Literature Review
3.1 Non-hybrid Nature-Inspired Techniques
3.2 Hybrid Nature-Inspired Techniques
4 The Steps for Developing Hybrid Nature-Inspired Techniques for Selecting the Optimal Web Service Composition Solution
5 Formal Definition
6 Hybridization Model
6.1 Core Components
6.2 Hybridization Components
7 Hybrid Selection Algorithms
7.1 The Hybrid Cuckoo Search-Based Algorithm
7.2 The Hybrid Firefly Search-Based Algorithm
8 Performance Evaluation
8.1 Setting the Optimal Values of the Adjustable Parameters
8.2 Evaluations of Hybridization
9 Conclusions
References
12 Geometric Firefly Algorithms on Graphical Processing Units
1 Introduction
2 Evolutionary Population-Based Combinatorial Optimisation
3 Candidate Representation Using Karva
4 GPU-Parallel Evolutionary Algorithms
5 GPU-Parallel Expression-Tree FA
6 Experimental Methodology
7 Selected Results
8 Visualisation
9 Discussion
10 Conclusions
References
13 A Discrete Firefly Algorithm for Scheduling Jobs on Computational Grid
1 Introduction
2 Related Work
3 Scheduling Problem Formulation
4 The Grid Brokering and Management Architecture
5 Job Scheduling Based on DFA
5.1 Firefly Algorithm
5.2 Discrete Firefly Algorithm
5.3 Scheduling Jobs using DFA
6 Implementation and Experimental Results
6.1 Simulation Framework
6.2 The Workloads
6.3 Parameter Selection
6.4 The Population Size
7 Conclusion and Future Work
References
14 A Parallelised Firefly Algorithm for Structural Size and Shape Optimisation with Multimodal Constraints
1 Introduction
1.1 Literature Review
2 The Firefly Algorithm
2.1 The Proposed Parallelised Versions of FA
3 Performance Metrics
4 Structural Size and Shape Optimization with Multimodal Constraints
5 Numerical Examples
5.1 Ten Bar Truss Problem
5.2 Shape and Size Optimisation of 37-bar Truss Problem
5.3 Shape and Size Optimisation of a 52-Bar Dome Problem
6 Final Remarks
References
15 Intelligent Firefly Algorithm for Global Optimization
1 Introduction
2 Firefly Algorithm (FA)
3 Intelligent Firefly Algorithm (IFA)
4 Numerical Experiments
5 Results and Discussion
6 Conclusions
References
16 Optimization of Queueing Structures by Firefly Algorithm
1 Introduction
2 Queueing Systems
2.1 M/M/m/-/m System with Losses
2.2 M/M/m/FIFO/m+N System with Finite Capacity and Impatient Customers
2.3 The M/M/m/FIFO/N/F Closed Queueing System with Finite Population of N Jobs
3 Optimization Problems
4 Firefly Algorithm
5 Results of Experiments
6 Conclusion
References
17 Firefly Algorithm: A Brief Review of the Expanding Literature
1 Introduction
2 Classifications of Firefly Algorithms
2.1 Modified FA
2.2 Hybrid Firefly Algorithms
3 Applications
3.1 Optimization
3.2 Classifications
4 Engineering Optimization
5 Conclusion
References
Studies in Computational Intelligence 516
Xin-She Yang Editor
Cuckoo Search
and Firefly
Algorithm
Theory and Applications
Studies in Computational Intelligence
Volume 516
Series Editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: kacprzyk@ibspan.waw.pl
For further volumes:
http://www.springer.com/series/7092
About this Series
The series ‘‘Studies in Computational Intelligence’’ (SCI) publishes new devel-
opments and advances in the various areas of computational intelligence—quickly
and with a high quality. The intent is to cover the theory, applications, and design
methods of computational intelligence, as embedded in the fields of engineering,
computer science, physics and life sciences, as well as the methodologies behind
them. The series contains monographs,
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computational intelligence spanning the areas of neural networks, connectionist
systems, genetic algorithms, evolutionary computation, artificial
intelligence,
cellular automata, self-organizing systems, soft computing, fuzzy systems, and
hybrid intelligent systems. Of particular value to both the contributors and the
readership are the short publication timeframe and the world-wide distribution,
which enable both wide and rapid dissemination of research output.
Xin-She Yang
Editor
Cuckoo Search and
Firefly Algorithm
Theory and Applications
123
Editor
Xin-She Yang
School of Science and Technology
Middlesex University
London
UK
ISSN 1860-949X
ISBN 978-3-319-02140-9
DOI 10.1007/978-3-319-02141-6
Springer Cham Heidelberg New York Dordrecht London
ISSN 1860-9503 (electronic)
ISBN 978-3-319-02141-6
(eBook)
Library of Congress Control Number: 2013953202
Ó Springer International Publishing Switzerland 2014
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of
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Preface
Many modelling and optimization problems require sophisticated algorithms to
solve. Contemporary optimization algorithms are often nature-inspired, based on
swarm intelligence. In the last two decades, there have been significant develop-
ments in the area of metaheuristic optimization and computational intelligence.
Optimization and computational intelligence have become ever-increasingly more
important. One of the core activities of the computational intelligence is that
‘‘intelligent’’ evolutionary algorithms play a vital role. Accompanying the progress
of computational
intelligence is the emergence of metaheuristic algorithms.
Among such algorithms, swarm-intelligence-based algorithms form a large part of
contemporary algorithms, and these algorithms are becoming widely used in
classifications, optimization, image processing, business intelligence as well as in
machine learning and computational intelligence.
Most new nature-inspired optimization algorithms are swarm-intelligence-based,
with multiple interacting agents. They are flexible, efficient and easy to implement.
For example, firefly algorithm (FA) was developed in late 2007 and early 2008 by
Xin-She Yang, based on the flashing behaviour of tropical fireflies, and FA has been
proved to be very efficient in solving multimodal, nonlinear, global optimization
problems. It is also very efficient in dealing with classification problems and image
processing. As another example, cuckoo search (CS) was developed by Xin-She
Yang and Suash Deb in 2009, based on the brooding parasitism of some cuckoo
species, in combination with Lévy flights, and CS is very efficient as demonstrated in
many studies by many researchers with diverse applications. In fact, at the time of
the writing in July 2013, there have been more than 440 research papers on cuckoo
search and 600 pagers on firefly algorithm in the literature, which shows that these
algorithms are indeed an active, hot research area.
This book strives to provide a timely summary of the latest developments
concerning cuckoo search and firefly algorithm with many contributions from
leading experts in the field. Topics include cuckoo search, firefly algorithm,
classifications, scheduling, feature selection, travelling salesman problem, neural
network training, semantic web service, multi-objective manufacturing process
optimization, parameter-tuning, queuing, randomization, reliability problem, GPU
optimization, shape optimization and others. This unique book can thus serve as an
ideal reference for both graduates and researchers in computer science, evolu-
tionary computing, machine learning, computational intelligence and optimization,
v
vi
Preface
as well as engineers in business intelligence, knowledge management and infor-
mation technology.
I would like to thank our Editors, Drs. Thomas Ditzinger and Holger Schaepe,
and staff at Springer for their help and professionalism. Last but not least, I thank
my family for the help and support.
London, July 2013
Xin-She Yang
Contents
Cuckoo Search and Firefly Algorithm: Overview and Analysis . . . . . .
Xin-She Yang
On the Randomized Firefly Algorithm . . . . . . . . . . . . . . . . . . . . . . . .
Iztok Fister, Xin-She Yang, Janez Brest and Iztok Fister Jr.
Cuckoo Search: A Brief Literature Review . . . . . . . . . . . . . . . . . . . .
Iztok Fister Jr., Xin-She Yang, Dušan Fister and Iztok Fister
Improved and Discrete Cuckoo Search for Solving the Travelling
Salesman Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Aziz Ouaarab, Belaïd Ahiod and Xin-She Yang
Comparative Analysis of the Cuckoo Search Algorithm . . . . . . . . . . .
Pinar Civicioglu and Erkan Besdok
Cuckoo Search and Firefly Algorithm Applied to Multilevel
Image Thresholding. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ivona Brajevic and Milan Tuba
A Binary Cuckoo Search and Its Application for Feature Selection. . .
L. A. M. Pereira, D. Rodrigues, T. N. S. Almeida, C. C. O. Ramos,
A. N. Souza, X.-S. Yang and J. P. Papa
How to Generate the Input Current for Exciting a Spiking
Neural Model Using the Cuckoo Search Algorithm. . . . . . . . . . . . . . .
Roberto A. Vazquez, Guillermo Sandoval and Jose Ambrosio
Multi-Objective Optimization of a Real-World Manufacturing
Process Using Cuckoo Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Anna Syberfeldt
Solving Reliability Optimization Problems by Cuckoo Search . . . . . . .
Ehsan Valian
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