Ant Colony Optimization and Constraint Programming

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Ant Colony Optimization and Constraint Programming"

Transcription

1 Ant Colony Optimization and Constraint Programming Christine Solnon Series Editor Narendra Jussien WILEY

2 Table of Contents Foreword Acknowledgements xi xiii Chapter 1. Introduction Overview of the book Constraint programming Ant colony optimization Constraint programming with ant colony optimization. 4 Chapter 2. Computational Complexity Complexity of an algorithm Complexity of a problem The V class ТЪеЛЛР class ЛЛР-complete problems ЛЛР-hard problems Undecidable problems Complexity of optimization problems Where the most difficult instances can be found Phase transition Search landscape Solving ЛГР-hard problems in practice Exploitation of particular cases Approximation algorithms Heuristics and metaheuristics Structuring and filtering the search space 24

3 vi ACO and CP PART I. CONSTRAINT PROGRAMMING 27 Introduction to Part I 29 Chapter 3. Constraint Satisfaction Problems What is a constraint? Definition of a constraint Arity of a constraint and global constraints What is a constraint satisfaction problem? Complexity of CSPs Optimization problems related to CSPs Maximizing constraint satisfaction Constrained optimization The n-queens problem Description of the problem First CSP model Second CSP model Third CSP model Influence of the model on the solution process The stable marriage problem Description of the problem CSP model Randomly generated binary CSPs The car sequencing problem Description of the problem CSP model Discussion 50 Chapter 4. Exact Approaches Construction of a search tree Constraint propagation Forward checking Maintaining arc consistency Ordering heuristics Heuristics for choosing variables Heuristics for choosing values Randomized restart From satisfaction to optimization problems Discussion 65

4 Table of Contents vii Chapter 5. Perturbative Heuristic Approaches Genetic algorithms Basic principles Using GAs to solve CSPs Local search Basic principles Metaheuristics based on LS Using LS to solve CSPs Particle swarm optimization Basic principles Using PSO to solve CSPs Discussion 80 Chapter 6. Constructive Heuristic Approaches Greedy randomized approaches Basic principles Using greedy randomized algorithms to solve CSPs Estimation of distribution algorithms Basic principles Using EDAs to solve CSPs Ant colony optimization Discussion 91 Chapter 7. Constraint Programming Languages Constraint logic programming Constraint programming libraries Constraint-based local search Discussion 99 PART II. ANT COLONY OPTIMIZATION 101 Introduction to Part II 103 Chapter 8. From Swarm Intelligence to Ant Colony Optimization Complex systems and swarm intelligence Searching for shortest paths by ant colonies Ant system and the traveling salesman problem Ill Pheromone structure Construction of a Hamiltonian cycle by an ant 114

5 viii ACO and CP Pheromone updating step Artificial versus real ants Generic ACO framework Pheromone structure and construction graph Construction of combinations by ants Improving combinations with local search Pheromone updating step Parameters of an ACO algorithm 122 Chapter 9. Intensification versus Diversification ACO mechanisms for intensifying the search ACO mechanisms for diversifying the search Balancing intensification and diversification Measures of diversification/intensification The A-branching factor Resampling ratio Similarity ratio 137 Chapter 10. Beyond Static Combinatorial Problems Multi-objective problems Definition of multi-objective problems Solving multi-objective problems with ACO Dynamic optimization problems Definition of dynamic optimization problems Solving dynamic optimization problems with ACO Optimization problems over continuous domains Definition of continuous optimization problems Solving continuous optimization problems with ACO. 148 Chapter 11. Implementation Issues Data structures Data structures associated with pheromone factors Data structures associated with heuristic factors Data structures associated with ants Selection of a component with respect to probabilities Implementation of a local search procedure Computation of diversification/intensification measures Resampling ratio Similarity ratio 158

6 Table of Contents ix PART III. CP WITH ACO 161 Introduction to Part III 163 Chapter 12. Sequencing Cars with ACO Notation A first pheromone structure for identifying good car sequences Pheromone structure Construction of a sequence by an ant Pheromone laying step A second pheromone structure for identifying critical cars Pheromone structure Construction of a sequence by an ant Pheromone updating step Combining the two pheromone structures First pheromone structure Second pheromone structure Construction of a sequence by an ant Comparison of the different ACO algorithms Considered algorithms Test suite Parameter settings Experimental results Comparison of ACO with state-of-the-art approaches Considered approaches IDWalk VFLS Experimental set-up Experimental results Discussion 182 Chapter 13. Subset Selection with ACO Subset selection problems Maximum clique Multidimensional knapsack Maximum Boolean satisfiability Maximum constraint satisfaction Minimum vertex cover Maximum common subgraph 188

7 x ACO and CP Edge-weighted fc-cardinality tree Description of Ant-SSP Construction of a combination by an ant Pheromone laying step Instantiations of Ant-SSP with respect to two pheromone strategies The vertex pheromone strategy The clique pheromone strategy Comparison of the two strategies Instantiation of Ant-SSP to solve CSPs Heuristic factor Local search Experimental results Randomly generated binary instances Results on instances of the 2006 solver competition Discussion 202 Chapter 14. Integration of ACO in a CP Language Framework for integrating ACO within a CP library Pheromone strategy Construction of assignments Pheromone updating step Illustration of ACO-CP on the car sequencing problem CSP model Variable ordering heuristic Pheromone strategies Heuristic factor Experimental results Discussion 214 Chapter 15. Conclusion Towards constraint-based ACO search Towards a reactive ACO search 216 Bibliography 219 Index 231

Integration of ACO in a Constraint Programming Language

Integration of ACO in a Constraint Programming Language Integration of ACO in a Constraint Programming Language Madjid Khichane 12, Patrick Albert 1, and Christine Solnon 2 1 ILOG 2 LIRIS, UMR 5205 CNRS / University of Lyon ANTS 08 Motivations Ant Colony Optimization

More information

Partitioning Constraint

Partitioning Constraint Tree-based Graph Partitioning Constraint Xavier Lorca Series Editor Narendra Jussien WILEY Table of Contents Part 1. Constraint Programming and Foundations of Graph Theory 1 Introduction to Part 1 3 Chapter

More information

Using Artificial Life Techniques to Generate Test Cases for Combinatorial Testing

Using Artificial Life Techniques to Generate Test Cases for Combinatorial Testing Using Artificial Life Techniques to Generate Test Cases for Combinatorial Testing Presentation: TheinLai Wong Authors: T. Shiba,, T. Tsuchiya, T. Kikuno Osaka University Backgrounds Testing is an important

More information

vii TABLE OF CONTENTS CHAPTER TITLE PAGE DECLARATION DEDICATION ACKNOWLEDGEMENT ABSTRACT ABSTRAK

vii TABLE OF CONTENTS CHAPTER TITLE PAGE DECLARATION DEDICATION ACKNOWLEDGEMENT ABSTRACT ABSTRAK vii TABLE OF CONTENTS CHAPTER TITLE PAGE DECLARATION DEDICATION ACKNOWLEDGEMENT ABSTRACT ABSTRAK TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS LIST OF SYMBOLS LIST OF APPENDICES

More information

Schneps, Leila; Colmez, Coralie. Math on Trial : How Numbers Get Used and Abused in the Courtroom. New York, NY, USA: Basic Books, 2013. p i.

Schneps, Leila; Colmez, Coralie. Math on Trial : How Numbers Get Used and Abused in the Courtroom. New York, NY, USA: Basic Books, 2013. p i. New York, NY, USA: Basic Books, 2013. p i. http://site.ebrary.com/lib/mcgill/doc?id=10665296&ppg=2 New York, NY, USA: Basic Books, 2013. p ii. http://site.ebrary.com/lib/mcgill/doc?id=10665296&ppg=3 New

More information

Projects - Neural and Evolutionary Computing

Projects - Neural and Evolutionary Computing Projects - Neural and Evolutionary Computing 2014-2015 I. Application oriented topics 1. Task scheduling in distributed systems. The aim is to assign a set of (independent or correlated) tasks to some

More information

STUDY OF PROJECT SCHEDULING AND RESOURCE ALLOCATION USING ANT COLONY OPTIMIZATION 1

STUDY OF PROJECT SCHEDULING AND RESOURCE ALLOCATION USING ANT COLONY OPTIMIZATION 1 STUDY OF PROJECT SCHEDULING AND RESOURCE ALLOCATION USING ANT COLONY OPTIMIZATION 1 Prajakta Joglekar, 2 Pallavi Jaiswal, 3 Vandana Jagtap Maharashtra Institute of Technology, Pune Email: 1 somanprajakta@gmail.com,

More information

CLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM

CLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM CLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM *Shabnam Ghasemi 1 and Mohammad Kalantari 2 1 Deparment of Computer Engineering, Islamic Azad University,

More information

How Can Metaheuristics Help Software Engineers

How Can Metaheuristics Help Software Engineers and Software How Can Help Software Engineers Enrique Alba eat@lcc.uma.es http://www.lcc.uma.es/~eat Universidad de Málaga, ESPAÑA Enrique Alba How Can Help Software Engineers of 8 and Software What s a

More information

MuACOsm A New Mutation-Based Ant Colony Optimization Algorithm for Learning Finite-State Machines

MuACOsm A New Mutation-Based Ant Colony Optimization Algorithm for Learning Finite-State Machines MuACOsm A New Mutation-Based Ant Colony Optimization Algorithm for Learning Finite-State Machines Daniil Chivilikhin and Vladimir Ulyantsev National Research University of IT, Mechanics and Optics St.

More information

Modelling for Constraint Programming

Modelling for Constraint Programming Modelling for Constraint Programming Barbara Smith 2. Implied Constraints, Optimization, Dominance Rules CP Summer School 28 Implied Constraints Implied constraints are logical consequences of the set

More information

An ACO/VNS Hybrid Approach for a Large-Scale Energy Management Problem

An ACO/VNS Hybrid Approach for a Large-Scale Energy Management Problem An ACO/VNS Hybrid Approach for a Large-Scale Energy Management Problem Challenge ROADEF/EURO 2010 Roman Steiner, Sandro Pirkwieser, Matthias Prandtstetter Vienna University of Technology, Austria Institute

More information

An ACO Approach to Solve a Variant of TSP

An ACO Approach to Solve a Variant of TSP An ACO Approach to Solve a Variant of TSP Bharat V. Chawda, Nitesh M. Sureja Abstract This study is an investigation on the application of Ant Colony Optimization to a variant of TSP. This paper presents

More information

Proposed Software Testing Using Intelligent techniques (Intelligent Water Drop (IWD) and Ant Colony Optimization Algorithm (ACO))

Proposed Software Testing Using Intelligent techniques (Intelligent Water Drop (IWD) and Ant Colony Optimization Algorithm (ACO)) www.ijcsi.org 91 Proposed Software Testing Using Intelligent techniques (Intelligent Water Drop (IWD) and Ant Colony Optimization Algorithm (ACO)) Laheeb M. Alzubaidy 1, Baraa S. Alhafid 2 1 Software Engineering,

More information

Heuristics for Dynamically Adapting Constraint Propagation in Constraint Programming

Heuristics for Dynamically Adapting Constraint Propagation in Constraint Programming Heuristics for Dynamically Adapting Constraint Propagation in Constraint Programming Kostas Stergiou AI Lab University of the Aegean Greece CPAIOR 09 Workshop on Bound reduction techniques for CP and MINLP

More information

An Ant Colony Optimization Approach to the Software Release Planning Problem

An Ant Colony Optimization Approach to the Software Release Planning Problem SBSE for Early Lifecyle Software Engineering 23 rd February 2011 London, UK An Ant Colony Optimization Approach to the Software Release Planning Problem with Dependent Requirements Jerffeson Teixeira de

More information

Optimization and Ranking in Web Service Composition using Performance Index

Optimization and Ranking in Web Service Composition using Performance Index Optimization and Ranking in Web Service Composition using Performance Index Pramodh N #1, Srinath V #2, Sri Krishna A #3 # Department of Computer Science and Engineering, SSN College of Engineering, Kalavakkam-

More information

Problem Solving in Philippe Codognet

Problem Solving in Philippe Codognet Problem Solving in Artificial Intelligence 4810-1208 Philippe Codognet SHORT INTRODUCTION TO THE COURSE TOPICS Lecturer Philippe CODOGNET Professor at University Pierre & Marie Curie (Paris) Co-Director

More information

An Improved ACO Algorithm for Multicast Routing

An Improved ACO Algorithm for Multicast Routing An Improved ACO Algorithm for Multicast Routing Ziqiang Wang and Dexian Zhang School of Information Science and Engineering, Henan University of Technology, Zheng Zhou 450052,China wzqagent@xinhuanet.com

More information

Using Ant Colony Optimization for Infrastructure Maintenance Scheduling

Using Ant Colony Optimization for Infrastructure Maintenance Scheduling Using Ant Colony Optimization for Infrastructure Maintenance Scheduling K. Lukas, A. Borrmann & E. Rank Chair for Computation in Engineering, Technische Universität München ABSTRACT: For the optimal planning

More information

Outline. NP-completeness. When is a problem easy? When is a problem hard? Today. Euler Circuits

Outline. NP-completeness. When is a problem easy? When is a problem hard? Today. Euler Circuits Outline NP-completeness Examples of Easy vs. Hard problems Euler circuit vs. Hamiltonian circuit Shortest Path vs. Longest Path 2-pairs sum vs. general Subset Sum Reducing one problem to another Clique

More information

Artificial Intelligence Methods (G52AIM)

Artificial Intelligence Methods (G52AIM) Artificial Intelligence Methods (G52AIM) Dr Rong Qu rxq@cs.nott.ac.uk Constructive Heuristic Methods Constructive Heuristics method Start from an empty solution Repeatedly, extend the current solution

More information

Meta-Heuristics for Reconstructing Cross Cut Shredded Text Documents

Meta-Heuristics for Reconstructing Cross Cut Shredded Text Documents Meta-Heuristics for Reconstructing Cross Cut Shredded Text Documents Matthias Prandtstetter Günther R. Raidl Institute of Computer Graphics and Algorithms Vienna University of Technology, Austria www.ads.tuwien.ac.at

More information

Schneps, Leila; Colmez, Coralie. Math on Trial : How Numbers Get Used and Abused in the Courtroom. New York, NY, USA: Basic Books, p i.

Schneps, Leila; Colmez, Coralie. Math on Trial : How Numbers Get Used and Abused in the Courtroom. New York, NY, USA: Basic Books, p i. New York, NY, USA: Basic Books, 2013. p i. http://site.ebrary.com/lib/mcgill/doc?id=10665296&ppg=2 New York, NY, USA: Basic Books, 2013. p iii. http://site.ebrary.com/lib/mcgill/doc?id=10665296&ppg=4 New

More information

HAROLD CAMPING i ii iii iv v vi vii viii ix x xi xii 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52

More information

Modified Ant Colony Optimization for Solving Traveling Salesman Problem

Modified Ant Colony Optimization for Solving Traveling Salesman Problem International Journal of Engineering & Computer Science IJECS-IJENS Vol:3 No:0 Modified Ant Colony Optimization for Solving Traveling Salesman Problem Abstract-- This paper presents a new algorithm for

More information

Ant Colony Optimization (ACO)

Ant Colony Optimization (ACO) Ant Colony Optimization (ACO) Exploits foraging behavior of ants Path optimization Problems mapping onto foraging are ACO-like TSP, ATSP QAP Travelling Salesman Problem (TSP) Why? Hard, shortest path problem

More information

AS-PSO, Ant Supervised by PSO Meta-heuristic with Application to TSP.

AS-PSO, Ant Supervised by PSO Meta-heuristic with Application to TSP. AS-PSO, Ant Supervised by PSO Meta-heuristic with Application to TSP. Nizar Rokbani *1, Arsene L. Momasso *2, Adel.M Alimi *3 # REGIM-Lab, Research Groups on Intelligent Machine University of Sfax, Tunisia

More information

TEST CASE SELECTION & PRIORITIZATION USING ANT COLONY OPTIMIZATION

TEST CASE SELECTION & PRIORITIZATION USING ANT COLONY OPTIMIZATION TEST CASE SELECTION & PRIORITIZATION USING ANT COLONY OPTIMIZATION Bharti Suri Computer Science Department Assistant Professor, USIT, GGSIPU New Delhi, India bhartisuri@gmail.com Shweta Singhal Information

More information

A novel ACO technique for Fast and Near Optimal Solutions for the Multi-dimensional Multi-choice Knapsack Problem

A novel ACO technique for Fast and Near Optimal Solutions for the Multi-dimensional Multi-choice Knapsack Problem A novel ACO technique for Fast and Near Optimal Solutions for the Multi-dimensional Multi-choice Knapsack Problem Shahrear Iqbal, Md. Faizul Bari, Dr. M. Sohel Rahman AlEDA Group Department of Computer

More information

Complexity Classes P and NP

Complexity Classes P and NP Complexity Classes P and NP MATH 3220 Supplemental Presentation by John Aleshunas The cure for boredom is curiosity. There is no cure for curiosity Dorothy Parker Computational Complexity Theory In computer

More information

Design and Analysis of ACO algorithms for edge matching problems

Design and Analysis of ACO algorithms for edge matching problems Design and Analysis of ACO algorithms for edge matching problems Carl Martin Dissing Söderlind Kgs. Lyngby 2010 DTU Informatics Department of Informatics and Mathematical Modelling Technical University

More information

NAIS: A Calibrated Immune Inspired Algorithm to solve Binary Constraint Satisfaction Problems

NAIS: A Calibrated Immune Inspired Algorithm to solve Binary Constraint Satisfaction Problems NAIS: A Calibrated Immune Inspired Algorithm to solve Binary Constraint Satisfaction Problems Marcos Zuñiga 1, María-Cristina Riff 2 and Elizabeth Montero 2 1 Projet ORION, INRIA Sophia-Antipolis Nice,

More information

Finding Liveness Errors with ACO

Finding Liveness Errors with ACO Hong Kong, June 1-6, 2008 1 / 24 Finding Liveness Errors with ACO Francisco Chicano and Enrique Alba Motivation Motivation Nowadays software is very complex An error in a software system can imply the

More information

Computer Algorithms. NP-Complete Problems. CISC 4080 Yanjun Li

Computer Algorithms. NP-Complete Problems. CISC 4080 Yanjun Li Computer Algorithms NP-Complete Problems NP-completeness The quest for efficient algorithms is about finding clever ways to bypass the process of exhaustive search, using clues from the input in order

More information

2.3 Scheduling jobs on identical parallel machines

2.3 Scheduling jobs on identical parallel machines 2.3 Scheduling jobs on identical parallel machines There are jobs to be processed, and there are identical machines (running in parallel) to which each job may be assigned Each job = 1,,, must be processed

More information

Obtaining Optimal Software Effort Estimation Data Using Feature Subset Selection

Obtaining Optimal Software Effort Estimation Data Using Feature Subset Selection Obtaining Optimal Software Effort Estimation Data Using Feature Subset Selection Abirami.R 1, Sujithra.S 2, Sathishkumar.P 3, Geethanjali.N 4 1, 2, 3 Student, Department of Computer Science and Engineering,

More information

A Binary Model on the Basis of Imperialist Competitive Algorithm in Order to Solve the Problem of Knapsack 1-0

A Binary Model on the Basis of Imperialist Competitive Algorithm in Order to Solve the Problem of Knapsack 1-0 212 International Conference on System Engineering and Modeling (ICSEM 212) IPCSIT vol. 34 (212) (212) IACSIT Press, Singapore A Binary Model on the Basis of Imperialist Competitive Algorithm in Order

More information

14.10.2014. Overview. Swarms in nature. Fish, birds, ants, termites, Introduction to swarm intelligence principles Particle Swarm Optimization (PSO)

14.10.2014. Overview. Swarms in nature. Fish, birds, ants, termites, Introduction to swarm intelligence principles Particle Swarm Optimization (PSO) Overview Kyrre Glette kyrrehg@ifi INF3490 Swarm Intelligence Particle Swarm Optimization Introduction to swarm intelligence principles Particle Swarm Optimization (PSO) 3 Swarms in nature Fish, birds,

More information

Complexity Theory. IE 661: Scheduling Theory Fall 2003 Satyaki Ghosh Dastidar

Complexity Theory. IE 661: Scheduling Theory Fall 2003 Satyaki Ghosh Dastidar Complexity Theory IE 661: Scheduling Theory Fall 2003 Satyaki Ghosh Dastidar Outline Goals Computation of Problems Concepts and Definitions Complexity Classes and Problems Polynomial Time Reductions Examples

More information

. 1/ CHAPTER- 4 SIMULATION RESULTS & DISCUSSION CHAPTER 4 SIMULATION RESULTS & DISCUSSION 4.1: ANT COLONY OPTIMIZATION BASED ON ESTIMATION OF DISTRIBUTION ACS possesses

More information

Study on Cloud Computing Resource Scheduling Strategy Based on the Ant Colony Optimization Algorithm

Study on Cloud Computing Resource Scheduling Strategy Based on the Ant Colony Optimization Algorithm www.ijcsi.org 54 Study on Cloud Computing Resource Scheduling Strategy Based on the Ant Colony Optimization Algorithm Linan Zhu 1, Qingshui Li 2, and Lingna He 3 1 College of Mechanical Engineering, Zhejiang

More information

Integrating Benders decomposition within Constraint Programming

Integrating Benders decomposition within Constraint Programming Integrating Benders decomposition within Constraint Programming Hadrien Cambazard, Narendra Jussien email: {hcambaza,jussien}@emn.fr École des Mines de Nantes, LINA CNRS FRE 2729 4 rue Alfred Kastler BP

More information

Department of Industrial Engineering

Department of Industrial Engineering Department of Industrial Engineering Master of Engineering Program in Industrial Engineering (International Program) M.Eng. (Industrial Engineering) Plan A Option 2: Total credits required: minimum 39

More information

Adaptation of the ACO heuristic for sequencing learning activities

Adaptation of the ACO heuristic for sequencing learning activities Adaptation of the ACO heuristic for sequencing learning activities Sergio Gutiérrez 1, Grégory Valigiani 2, Pierre Collet 2 and Carlos Delgado Kloos 1 1 University Carlos III of Madrid (Spain) 2 Université

More information

Binary Encodings of Non-binary Constraint Satisfaction Problems: Algorithms and Experimental Results

Binary Encodings of Non-binary Constraint Satisfaction Problems: Algorithms and Experimental Results Journal of Artificial Intelligence Research 24 (2005) 641-684 Submitted 04/05; published 11/05 Binary Encodings of Non-binary Constraint Satisfaction Problems: Algorithms and Experimental Results Nikolaos

More information

An ACO-based Approach for Scheduling Task Graphs with Communication Costs

An ACO-based Approach for Scheduling Task Graphs with Communication Costs An ACO-based Approach for Scheduling Task Graphs with Communication Costs Markus Bank Udo Hönig Wolfram Schiffmann FernUniversität Hagen Lehrgebiet Rechnerarchitektur 58084 Hagen, Germany {Markus.Bank,

More information

Swarm Intelligence Algorithms Parameter Tuning

Swarm Intelligence Algorithms Parameter Tuning Swarm Intelligence Algorithms Parameter Tuning Milan TUBA Faculty of Computer Science Megatrend University of Belgrade Bulevar umetnosti 29, N. Belgrade SERBIA tuba@ieee.org Abstract: - Nature inspired

More information

Journal of Theoretical and Applied Information Technology 20 th July 2015. Vol.77. No.2 2005-2015 JATIT & LLS. All rights reserved.

Journal of Theoretical and Applied Information Technology 20 th July 2015. Vol.77. No.2 2005-2015 JATIT & LLS. All rights reserved. EFFICIENT LOAD BALANCING USING ANT COLONY OPTIMIZATION MOHAMMAD H. NADIMI-SHAHRAKI, ELNAZ SHAFIGH FARD, FARAMARZ SAFI Department of Computer Engineering, Najafabad branch, Islamic Azad University, Najafabad,

More information

ITEC2620 Introduction to Data Structures

ITEC2620 Introduction to Data Structures ITEC2620 Introduction to Data Structures Lecture 8b Search Trees Traversals Typical covered as graph traversals in data structures courses I ve never seen a graph traversal in practice! AI background Tree

More information

npsolver A SAT Based Solver for Optimization Problems

npsolver A SAT Based Solver for Optimization Problems npsolver A SAT Based Solver for Optimization Problems Norbert Manthey and Peter Steinke Knowledge Representation and Reasoning Group Technische Universität Dresden, 01062 Dresden, Germany peter@janeway.inf.tu-dresden.de

More information

A Survey on Load Balancing Techniques Using ACO Algorithm

A Survey on Load Balancing Techniques Using ACO Algorithm A Survey on Load Balancing Techniques Using ACO Algorithm Preeti Kushwah Department of Computer Science & Engineering, Acropolis Institute of Technology and Research Indore bypass road Mangliya square

More information

Extended Abstract of the Doctoral Thesis: The Complexity and Expressive Power of Valued Constraints

Extended Abstract of the Doctoral Thesis: The Complexity and Expressive Power of Valued Constraints Extended Abstract of the Doctoral Thesis: The Complexity and Expressive Power of Valued Constraints Stanislav Živný 1 Introduction Constraint programming is a powerful paradigm for solving combinatorial

More information

Solving the Travelling Salesman Problem Using the Ant Colony Optimization

Solving the Travelling Salesman Problem Using the Ant Colony Optimization Ivan Brezina Jr. Zuzana Čičková Solving the Travelling Salesman Problem Using the Ant Colony Optimization Article Info:, Vol. 6 (2011), No. 4, pp. 010-014 Received 12 July 2010 Accepted 23 September 2011

More information

Research on SQLite Database Query Optimization Based on Improved PSO Algorithm

Research on SQLite Database Query Optimization Based on Improved PSO Algorithm , pp.239-246 http://dx.doi.org/10.14257/ijdta.2016.9.4.22 Research on SQLite Database Query Optimization Based on Improved PSO Algorithm Aite Zhao 1, Zhiqiang Wei 1 and Yongquan Yang 1,* 1 Ocean University

More information

A Novel Binary Particle Swarm Optimization

A Novel Binary Particle Swarm Optimization Proceedings of the 5th Mediterranean Conference on T33- A Novel Binary Particle Swarm Optimization Motaba Ahmadieh Khanesar, Member, IEEE, Mohammad Teshnehlab and Mahdi Aliyari Shoorehdeli K. N. Toosi

More information

Constraint Satisfaction Problems. Constraint Satisfaction Problems. Greedy Local Search. Example. 1 Greedy algorithm. 2 Stochastic Greedy Local Search

Constraint Satisfaction Problems. Constraint Satisfaction Problems. Greedy Local Search. Example. 1 Greedy algorithm. 2 Stochastic Greedy Local Search Constraint Satisfaction Problems June 19, 2007 Greedy Local Search Constraint Satisfaction Problems Greedy Local Search Bernhard Nebel, Julien Hué, and Stefan Wölfl Albert-Ludwigs-Universität Freiburg

More information

Y. Xiang, Constraint Satisfaction Problems

Y. Xiang, Constraint Satisfaction Problems Constraint Satisfaction Problems Objectives Constraint satisfaction problems Backtracking Iterative improvement Constraint propagation Reference Russell & Norvig: Chapter 5. 1 Constraints Constraints are

More information

Hybrid Algorithm using the advantage of ACO and Cuckoo Search for Job Scheduling

Hybrid Algorithm using the advantage of ACO and Cuckoo Search for Job Scheduling Hybrid Algorithm using the advantage of ACO and Cuckoo Search for Job Scheduling R.G. Babukartik 1, P. Dhavachelvan 1 1 Department of Computer Science, Pondicherry University, Pondicherry, India {r.g.babukarthik,

More information

Global Multiprocessor Real-Time Scheduling as a Constraint Satisfaction Problem

Global Multiprocessor Real-Time Scheduling as a Constraint Satisfaction Problem Global Multiprocessor Real-Time Scheduling as a Constraint Satisfaction Problem Liliana Cucu-Grosean & Olivier Buffet INRIA Nancy Grand-Est 615 rue du Jardin Botanique 54600 Villers-lès-Nancy, France firstname.lastname@loria.fr

More information

QoS Guaranteed Intelligent Routing Using Hybrid PSO-GA in Wireless Mesh Networks

QoS Guaranteed Intelligent Routing Using Hybrid PSO-GA in Wireless Mesh Networks BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 15, No 1 Sofia 2015 Print ISSN: 1311-9702; Online ISSN: 1314-4081 DOI: 10.1515/cait-2015-0007 QoS Guaranteed Intelligent Routing

More information

A Novel ACO Algorithm for Dynamic Binary Chains based on Changes in the System s Stability

A Novel ACO Algorithm for Dynamic Binary Chains based on Changes in the System s Stability A Novel ACO Algorithm for Dynamic Binary Chains based on Changes in the System s Stability Claudio Iacopino and Phil Palmer Surrey Space Centre, University of Surrey Guildford, GU2 7XH, United Kingdom

More information

Adaptive Memory Search for Boolean Optimization Problems

Adaptive Memory Search for Boolean Optimization Problems Adaptive Memory Search for Boolean Optimization Problems Lars M. Hvattum Molde College, 6411 Molde, Norway. Lars.M.Hvattum@himolde.no Arne Løkketangen Molde College, 6411 Molde, Norway. Arne.Lokketangen@himolde.no

More information

A Constraint-Based Method for Project Scheduling with Time Windows

A Constraint-Based Method for Project Scheduling with Time Windows A Constraint-Based Method for Project Scheduling with Time Windows Amedeo Cesta 1 and Angelo Oddi 1 and Stephen F. Smith 2 1 ISTC-CNR, National Research Council of Italy Viale Marx 15, I-00137 Rome, Italy,

More information

Multi-Objective Ant Colony Optimization for Solving the Twin- Screw Extrusion Configuration Problem

Multi-Objective Ant Colony Optimization for Solving the Twin- Screw Extrusion Configuration Problem Multi-Objective Ant Colony Optimization for Solving the Twin- Screw Extrusion Configuration Problem Cristina Teixeira*, J. A. Covas*, Thomas Stützle** and A. Gaspar-Cunha* *IPC/I3N-Institute for Polymer

More information

Bachelor Thesis Performance comparison of heuristic algorithms in routing optimization of sequencing traversing cars in a warehouse

Bachelor Thesis Performance comparison of heuristic algorithms in routing optimization of sequencing traversing cars in a warehouse Bachelor Thesis Performance comparison of heuristic algorithms in routing optimization of sequencing traversing cars in a warehouse Minh Hoang Technische Universität Hamburg-Harburg supervised by: Prof.

More information

AN APPROACH FOR SOFTWARE TEST CASE SELECTION USING HYBRID PSO

AN APPROACH FOR SOFTWARE TEST CASE SELECTION USING HYBRID PSO INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 AN APPROACH FOR SOFTWARE TEST CASE SELECTION USING HYBRID PSO 1 Preeti Bala Thakur, 2 Prof. Toran Verma 1 Dept. of

More information

Requirements Selection: Knowledge based optimization techniques for solving the Next Release Problem

Requirements Selection: Knowledge based optimization techniques for solving the Next Release Problem Requirements Selection: Knowledge based optimization techniques for solving the Next Release Problem José del Sagrado, Isabel M. del Águila, Francisco J. Orellana, and S. Túnez Dpt. Languages and Computation,

More information

Representing and Solving Rule-Based Decision Models with Constraint Solvers

Representing and Solving Rule-Based Decision Models with Constraint Solvers Representing and Solving Rule-Based Decision Models with Constraint Solvers Jacob Feldman OpenRules, Inc., 75 Chatsworth Ct., Edison, NJ 08820, USA jacobfeldman@openrules.com Abstract. This paper describes

More information

ACO Based Dynamic Resource Scheduling for Improving Cloud Performance

ACO Based Dynamic Resource Scheduling for Improving Cloud Performance ACO Based Dynamic Resource Scheduling for Improving Cloud Performance Priyanka Mod 1, Prof. Mayank Bhatt 2 Computer Science Engineering Rishiraj Institute of Technology 1 Computer Science Engineering Rishiraj

More information

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET)

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 6367(Print), ISSN 0976 6367(Print) ISSN 0976 6375(Online)

More information

Scheduling Home Health Care with Separating Benders Cuts in Decision Diagrams

Scheduling Home Health Care with Separating Benders Cuts in Decision Diagrams Scheduling Home Health Care with Separating Benders Cuts in Decision Diagrams André Ciré University of Toronto John Hooker Carnegie Mellon University INFORMS 2014 Home Health Care Home health care delivery

More information

CHAPTER 6 MAJOR RESULTS AND CONCLUSIONS

CHAPTER 6 MAJOR RESULTS AND CONCLUSIONS 133 CHAPTER 6 MAJOR RESULTS AND CONCLUSIONS The proposed scheduling algorithms along with the heuristic intensive weightage factors, parameters and ß and their impact on the performance of the algorithms

More information

EA and ACO Algorithms Applied to Optimizing Location of Controllers in Wireless Networks

EA and ACO Algorithms Applied to Optimizing Location of Controllers in Wireless Networks 2 EA and ACO Algorithms Applied to Optimizing Location of Controllers in Wireless Networks Dac-Nhuong Le, Hanoi University of Science, Vietnam National University, Vietnam Optimizing location of controllers

More information

The Problem of Scheduling Technicians and Interventions in a Telecommunications Company

The Problem of Scheduling Technicians and Interventions in a Telecommunications Company The Problem of Scheduling Technicians and Interventions in a Telecommunications Company Sérgio Garcia Panzo Dongala November 2008 Abstract In 2007 the challenge organized by the French Society of Operational

More information

Disjunction of Non-Binary and Numeric Constraint Satisfaction Problems

Disjunction of Non-Binary and Numeric Constraint Satisfaction Problems Disjunction of Non-Binary and Numeric Constraint Satisfaction Problems Miguel A. Salido, Federico Barber Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia Camino

More information

An Improved Ant Colony Optimization Algorithm for Software Project Planning and Scheduling

An Improved Ant Colony Optimization Algorithm for Software Project Planning and Scheduling An Improved Ant Colony Optimization Algorithm for Software Project Planning and Scheduling Avinash Mahadik Department Of Computer Engineering Alard College Of Engineering And Management,Marunje, Pune Email-avinash.mahadik5@gmail.com

More information

NP-Completeness. CptS 223 Advanced Data Structures. Larry Holder School of Electrical Engineering and Computer Science Washington State University

NP-Completeness. CptS 223 Advanced Data Structures. Larry Holder School of Electrical Engineering and Computer Science Washington State University NP-Completeness CptS 223 Advanced Data Structures Larry Holder School of Electrical Engineering and Computer Science Washington State University 1 Hard Graph Problems Hard means no known solutions with

More information

Web Mining using Artificial Ant Colonies : A Survey

Web Mining using Artificial Ant Colonies : A Survey Web Mining using Artificial Ant Colonies : A Survey Richa Gupta Department of Computer Science University of Delhi ABSTRACT : Web mining has been very crucial to any organization as it provides useful

More information

Smart Graphics: Methoden 3 Suche, Constraints

Smart Graphics: Methoden 3 Suche, Constraints Smart Graphics: Methoden 3 Suche, Constraints Vorlesung Smart Graphics LMU München Medieninformatik Butz/Boring Smart Graphics SS2007 Methoden: Suche 2 Folie 1 Themen heute Suchverfahren Hillclimbing Simulated

More information

Biogeography Based Optimization (BBO) Approach for Sensor Selection in Aircraft Engine

Biogeography Based Optimization (BBO) Approach for Sensor Selection in Aircraft Engine Biogeography Based Optimization (BBO) Approach for Sensor Selection in Aircraft Engine V.Hymavathi, B.Abdul Rahim, Fahimuddin.Shaik P.G Scholar, (M.Tech), Department of Electronics and Communication Engineering,

More information

On the Empirical Evaluation of Las Vegas Algorithms Position Paper

On the Empirical Evaluation of Las Vegas Algorithms Position Paper On the Empirical Evaluation of Las Vegas Algorithms Position Paper Holger Hoos ½ Computer Science Department University of British Columbia Email: hoos@cs.ubc.ca Thomas Stützle IRIDIA Université Libre

More information

Performance Evaluation of Task Scheduling in Cloud Environment Using Soft Computing Algorithms

Performance Evaluation of Task Scheduling in Cloud Environment Using Soft Computing Algorithms 387 Performance Evaluation of Task Scheduling in Cloud Environment Using Soft Computing Algorithms 1 R. Jemina Priyadarsini, 2 Dr. L. Arockiam 1 Department of Computer science, St. Joseph s College, Trichirapalli,

More information

Multi-Robot Traffic Planning Using ACO

Multi-Robot Traffic Planning Using ACO Multi-Robot Traffic Planning Using ACO DR. ANUPAM SHUKLA, SANYAM AGARWAL ABV-Indian Institute of Information Technology and Management, Gwalior INDIA Sanyam.iiitm@gmail.com Abstract: - Path planning is

More information

Combinatorial Optimization and the Analysis of Randomized Search Heuristics

Combinatorial Optimization and the Analysis of Randomized Search Heuristics Combinatorial Optimization and the Analysis of Randomized Search Heuristics Dissertation zur Erlangung des akademischen Grades Doktor der Ingenieurwissenschaften (Dr. Ing.) der Technischen Fakultät der

More information

Replicating Multi-quality Web Applications Using ACO and Bipartite Graphs

Replicating Multi-quality Web Applications Using ACO and Bipartite Graphs Replicating Multi-quality Web Applications Using ACO and Bipartite Graphs Christopher B. Mayer 1, Judson Dressler 1, Felicia Harlow 1, Gregory Brault 1, and K. Selçuk Candan 2 1 Department of Electrical

More information

The car sequencing problem: overview of state-of-the-art methods and industrial case-study of the ROADEF 2005 challenge problem

The car sequencing problem: overview of state-of-the-art methods and industrial case-study of the ROADEF 2005 challenge problem The car sequencing problem: overview of state-of-the-art methods and industrial case-study of the ROADEF 2005 challenge problem Christine Solnon, Van-Dat Cung, Alain Nguyen, Christian Artigues To cite

More information

Lecture 2. Uninformed search

Lecture 2. Uninformed search Lecture 2 Uninformed search " Reference: Preparing for week 1 Reading: Chapters 1 and 2.1, 2.2, 2.5, 3.1, 3.2, 3.3, 3.4, 3.5 Assignment 1 has now been posted on the course LEARN site Uses MATLAB (a tutorial

More information

Car Sequencing with Constraint-Based ACO

Car Sequencing with Constraint-Based ACO Car Sequencing with Constraint-Based ACO Dhananjay Thiruvady Clayton School of Information Technology, Monash University, Australia dhananjay.thiruvady@- monash.edu Bernd Meyer Clayton School of Information

More information

Research Paper Business Analytics. Applications for the Vehicle Routing Problem. Jelmer Blok

Research Paper Business Analytics. Applications for the Vehicle Routing Problem. Jelmer Blok Research Paper Business Analytics Applications for the Vehicle Routing Problem Jelmer Blok Applications for the Vehicle Routing Problem Jelmer Blok Research Paper Vrije Universiteit Amsterdam Faculteit

More information

HYBRID ACO-IWD OPTIMIZATION ALGORITHM FOR MINIMIZING WEIGHTED FLOWTIME IN CLOUD-BASED PARAMETER SWEEP EXPERIMENTS

HYBRID ACO-IWD OPTIMIZATION ALGORITHM FOR MINIMIZING WEIGHTED FLOWTIME IN CLOUD-BASED PARAMETER SWEEP EXPERIMENTS HYBRID ACO-IWD OPTIMIZATION ALGORITHM FOR MINIMIZING WEIGHTED FLOWTIME IN CLOUD-BASED PARAMETER SWEEP EXPERIMENTS R. Angel Preethima 1, Margret Johnson 2 1 Student, Computer Science and Engineering, Karunya

More information

Multi-Objective Supply Chain Model through an Ant Colony Optimization Approach

Multi-Objective Supply Chain Model through an Ant Colony Optimization Approach Multi-Objective Supply Chain Model through an Ant Colony Optimization Approach Anamika K. Mittal L. D. College of Engineering, Ahmedabad, India Chirag S. Thaker L. D. College of Engineering, Ahmedabad,

More information

The Use of Cuckoo Search in Estimating the Parameters of Software Reliability Growth Models

The Use of Cuckoo Search in Estimating the Parameters of Software Reliability Growth Models The Use of Cuckoo Search in Estimating the Parameters of Software Reliability Growth Models Dr. Najla Akram AL-Saati and Marwa Abd-ALKareem Software Engineering Dept. College of Computer Sciences & Mathematics,

More information

Page 1. CSCE 310J Data Structures & Algorithms. CSCE 310J Data Structures & Algorithms. P, NP, and NP-Complete. Polynomial-Time Algorithms

Page 1. CSCE 310J Data Structures & Algorithms. CSCE 310J Data Structures & Algorithms. P, NP, and NP-Complete. Polynomial-Time Algorithms CSCE 310J Data Structures & Algorithms P, NP, and NP-Complete Dr. Steve Goddard goddard@cse.unl.edu CSCE 310J Data Structures & Algorithms Giving credit where credit is due:» Most of the lecture notes

More information

Computer based Scheduling Tool for Multi-product Scheduling Problems

Computer based Scheduling Tool for Multi-product Scheduling Problems Computer based Scheduling Tool for Multi-product Scheduling Problems Computer based Scheduling Tool for Multi-product Scheduling Problems Adirake Chainual, Tawatchai Lutuksin and Pupong Pongcharoen Department

More information

Constraint-Based Local Search for Constrained Optimum Paths Problems

Constraint-Based Local Search for Constrained Optimum Paths Problems Constraint-Based Local Search for Constrained Optimum Paths Problems Quang Dung PHAM 1, Yves DEVILLE 1, Pascal Van HENTENRYCK 2 1 Université catholique de Louvain B-1348 Louvain-la-Neuve, Belgium {quang.pham,yves.deville}@uclouvain.be

More information

A Multi-Objective Extremal Optimisation Approach Applied to RFID Antenna Design

A Multi-Objective Extremal Optimisation Approach Applied to RFID Antenna Design A Multi-Objective Extremal Optimisation Approach Applied to RFID Antenna Design Pedro Gómez-Meneses, Marcus Randall and Andrew Lewis Abstract Extremal Optimisation (EO) is a recent nature-inspired meta-heuristic

More information

2. (a) Explain the strassen s matrix multiplication. (b) Write deletion algorithm, of Binary search tree. [8+8]

2. (a) Explain the strassen s matrix multiplication. (b) Write deletion algorithm, of Binary search tree. [8+8] Code No: R05220502 Set No. 1 1. (a) Describe the performance analysis in detail. (b) Show that f 1 (n)+f 2 (n) = 0(max(g 1 (n), g 2 (n)) where f 1 (n) = 0(g 1 (n)) and f 2 (n) = 0(g 2 (n)). [8+8] 2. (a)

More information

A New Implementation of Mathematical Models With Metahuristic Algorithms For Business Intelligence

A New Implementation of Mathematical Models With Metahuristic Algorithms For Business Intelligence A New Implementation of Mathematical Models With Metahuristic Algorithms For Business Intelligence Mary Jeyanthi Prem 1, M.Karnan 2 VELS University, Pallavaram, Chennai, Tamil Nadu, India 1 Tamil Nadu

More information

INTEGER PROGRAMMING. Integer Programming. Prototype example. BIP model. BIP models

INTEGER PROGRAMMING. Integer Programming. Prototype example. BIP model. BIP models Integer Programming INTEGER PROGRAMMING In many problems the decision variables must have integer values. Example: assign people, machines, and vehicles to activities in integer quantities. If this is

More information