Metaheuristics and Cognitive Models for Autonomous Robot Navigation

Size: px
Start display at page:

Download "Metaheuristics and Cognitive Models for Autonomous Robot Navigation"

Transcription

1 Metaheuristics and Cognitive Models for Autonomous Robot Navigation Raj Korpan Department of Computer Science The Graduate Center, CUNY Second Exam Presentation April 25, / 31

2 Autonomous robot navigation (ARN) Mobile robots move through an environment from one location to another without human intervention Two heuristic-based approaches Metaheuristics for path planning Cognitive models of human navigation These approaches Learn about the search space or the environment Balance the tradeoff between exploration and exploitation 2 / 31

3 Outline Background Challenges in ARN Heuristics and metaheuristics Cognitive science Conclusions 3 / 31

4 Outline Background Challenges in ARN Heuristics and metaheuristics Cognitive science Conclusions 4 / 31

5 Background Problem P = S, I, A, G S: set of states I S: set of initial states A: set of possible actions a A G s is a Boolean goal test Problem domain = set of related problems that share some characteristic Path = a finite ordered sequence of interleaved states and actions s 1, a 1, s 2, a 2,, s r 1, a r 1, s r Solution = a path where s 1 I and G s r = True Optimal solution has minimum path cost Satisfactory solution is good enough 5 / 31

6 Path planning Plan = path proved to be a solution before execution Path planning = search for a plan that minimizes travel time, travel distance, or resource consumption Path quality = value of a path based on domain-specific criteria Goal state Solution Satisfactory solutions Optimal solution? Initial state 6 / 31

7 Outline Background Challenges in ARN Heuristics and metaheuristics Cognitive science Conclusions 7 / 31

8 Challenges in ARN (1) Path planning = how do I get to where I need to go? Localization = where am I in the environment? How do I detect my pose x, y, θ despite sensor error? Mapping = what does the environment look like? Obstacle avoidance = how do I get around obstructions in the environment? Motion control = how do I account for error from my actuators? NP-Hard [Canny, 1988] Simultaneous localization and mapping (SLAM) [Durrant, 2006] Reactive and online Handled by hardware Current state-of-the-art for implemented ARN systems on commercial robots = SLAM with A* search Lack of standard testbed and performance metrics no conclusive evidence that any approach is best Since sub-optimal solutions must suffice many other approaches have been investigated 8 / 31

9 Challenges in ARN (2) Real world vs. simulation Environmental issues = observability, multiagency, dynamism, continuity, and modality [Bartel, 2007] [Zhou, 2014] 9 / 31

10 Outline Background Challenges in ARN Heuristics and metaheuristics Cognitive science Conclusions 10 / 31

11 Heuristics and metaheuristics Heuristic = efficient strategy that can often solve problems, typically specific to a problem domain [Pearl, 1984] Metaheuristic = a broadly applicable and non-domain specific technique that uses a heuristic strategy to obtain satisfactory solutions [Glover, 2006] Hybrid metaheuristic = 2 metaheuristics or metaheuristic + another method [Blum, 2011] No guarantee that an optimal solution will be found in finite time satisfactory solutions suffice in path planning for ARN Candidate = path found during search Neighbor of a candidate = path with one change to the candidate s action sequence changed added removed 11 / 31

12 Taxonomy of search methods Single solution Number of candidates considered Population Local Hill climbing (HC) Steepest-ascent HC Simulated annealing Tabu search Swarm algorithms Other Locality of search Both Heuristic-based methods Metaheuristics Evolutionary algorithms Ant colony optimization Artificial bee colony Firefly algorithm Global Random search HC with random restarts Genetic algorithm Genetic programming Evolutionary programming Differential evolution Biogeography-based optimization Particle swarm optimization Bat algorithm Cuckoo search algorithm [Talbi, 2009] Gravitational search Memetic algorithm metaheuristics Harmony search 12 / 31

13 Taxonomy of search methods Single solution Number of candidates considered Population Local Hill climbing (HC) Steepest-ascent HC Simulated annealing Tabu search Swarm algorithms Other Locality of search Both Heuristic-based methods Metaheuristics Evolutionary algorithms Ant colony optimization Artificial bee colony Firefly algorithm Global Random search HC with random restarts Genetic algorithm Genetic programming Evolutionary programming Differential evolution Biogeography-based optimization Particle swarm optimization Bat algorithm Cuckoo search algorithm [Talbi, 2009] Gravitational search Memetic algorithm metaheuristics Harmony search 13 / 31

14 Heuristic-based methods Random search Pure exploration Hill climbing Pure exploitation of learned knowledge Hill climbing with random restarts Random exploration with periods of exploitation Hill climbing with steepest ascent Exploitation of learned knowledge with limited exploration around candidate Fast but highly susceptible to premature convergence to local optima 14 / 31

15 Taxonomy of search methods Single solution Number of candidates considered Population Local Hill climbing (HC) Steepest-ascent HC Simulated annealing Tabu search Swarm algorithms Other Locality of search Both Heuristic-based methods Metaheuristics Evolutionary algorithms Ant colony optimization Artificial bee colony Firefly algorithm Global Random search HC with random restarts Genetic algorithm Genetic programming Evolutionary programming Differential evolution Biogeography-based optimization Particle swarm optimization Bat algorithm Cuckoo search algorithm [Talbi, 2009] Gravitational search Memetic algorithm metaheuristics Harmony search 15 / 31

16 Single-solution metaheuristics Simulated annealing improves hill climbing [Kirkpatrick, 1983; Černỳ, 1985] Occasionally selects candidates with lower quality Changes the balance of exploration and exploitation by reducing the probability of selecting a lower quality candidates over time Tabu search also selects lower quality candidates [Glover, 1989; 1990] Prevents return to recently visited candidates Intentionally limits exploitation to increase exploration Advantages: efficient and can control memory requirements Disadvantages: highly dependent on initialization and parameter settings, may be slow Applications to path planning are rare and simulated in simplistic, unrealistic environments 16 / 31

17 Taxonomy of search methods Single solution Number of candidates considered Population Local Hill climbing (HC) Steepest-ascent HC Simulated annealing Tabu search Swarm algorithms Other Locality of search Both Heuristic-based methods Metaheuristics Evolutionary algorithms Ant colony optimization Artificial bee colony Firefly algorithm Global Random search HC with random restarts Genetic algorithm Genetic programming Evolutionary programming Differential evolution Biogeography-based optimization Particle swarm optimization Bat algorithm Cuckoo search algorithm [Talbi, 2009] Gravitational search Memetic algorithm metaheuristics Harmony search 17 / 31

18 Evolutionary algorithms (1) Inspired by Darwinian principles Genetic algorithm [Manikas, 2007] Evolves a population of candidates with reproduction, crossover, mutation, and selection Fitness function evaluates quality of candidates Genetic programming = candidates are computer programs [Koza, 1992] Evolutionary programming [Fogel, 1999] Candidates are value assignments to parameters of a single program The next generation is composed of the best candidates selected from among the parents and children Differential evolution [Storn, 1997] Candidates are vectors of real numbers that are function parameters Recombination incorporates a third population member during reproduction 18 / 31

19 Evolutionary algorithms (2) Applications to path planning Single robot and unmanned aerial vehicle (UAV) Multi-robot and multi-uav Multi-objective Addition of domain knowledge Advantages: incorporates exploitation/hill-climbing through selection, and exploration/randomization through mutation Disadvantages: Parameters must be hand tuned Can be computationally and memory intensive 19 / 31

20 Taxonomy of search methods Single solution Number of candidates considered Population Local Hill climbing (HC) Steepest-ascent HC Simulated annealing Tabu search Swarm algorithms Other Locality of search Both Heuristic-based methods Metaheuristics Evolutionary algorithms Ant colony optimization Artificial bee colony Firefly algorithm Global Random search HC with random restarts Genetic algorithm Genetic programming Evolutionary programming Differential evolution Biogeography-based optimization Particle swarm optimization Bat algorithm Cuckoo search algorithm [Talbi, 2009] Gravitational search Memetic algorithm metaheuristics Harmony search 20 / 31

21 Swarm algorithms Simulates crowd behavior of organisms Ant colony optimization [Dorigo, 2006] Indirect communication when foraging Individual ants use local search Pheromones indicate candidate desirability Artificial bee colony [Karaboga, 2007] Three groups: employed bees, onlookers, and scouts Combines local search and global search Particle swarm optimization [Kennedy, 1995] Birds in search of food as particles that move through search space Direction of each particle s local search is influenced by other particles Other approaches: firefly algorithm, bat algorithm, cuckoo search 21 / 31

22 Swarm algorithms comparison Algorithm Individuals Type of search Communication mechanism Ant colony optimization Artificial bee colony Particle swarm optimization Firefly algorithm ants hill climbing pheromone strength bees hill climbing and random search dance intensity particles hill climbing velocity of global best position fireflies hill climbing flashing brightness and distance Bat algorithm bats hill climbing and random search Cuckoo search cuckoos random search none echolocation frequency, loudness, and rate of emissions 22 / 31

23 Path planning with swarm algorithms Applications Single robot, UAV, and underwater Multi-robot and multi-uav Multi-objective Addition of domain knowledge Dynamic obstacles Advantages: communication mechanisms allow local information to influence entire population Disadvantages: Parameters must be hand tuned Can be computationally and memory intensive 23 / 31

24 Hybrid metaheuristics Categorized by Type of hybrid: dual (2 metaheuristics) or metaheuristic + another method Approach to ARN: blend components into a new system or use them separately for different tasks Applications to path planning Mostly in static, simulated environments Dual hybrids typically evolutionary algorithm + swarm algorithm Other methods include probabilistic roadmap, chaotic search, fuzzy logic, and artificial potential field Advantages: tries to resolve shortcomings of individual methods by combining multiple methods Disadvantages: Difficult to compare approaches Parameters must be hand tuned Increased computational complexity 24 / 31

25 Outline Background Challenges in ARN Heuristics and metaheuristics Cognitive science Conclusions 25 / 31

26 Cognitive science Multidisciplinary field that studies the mind and intelligence [Friedenberg, 2011] Spatial cognition = subfield of cognitive science that studies navigation and wayfinding Decision making = agent selects among a set of alternatives Reasoning = agent draws a conclusion from information to solve a problem or make a decision People exhibit goal-directed behavior People use heuristics to make decisions, especially when faced with limited time, knowledge, or computational power [Gigerenzer, 1999] People use case-based reasoning People employ hierarchical organization of spatial memory [Wiener, 2003] 26 / 31

27 Human navigation Strategies for human navigation What? Spatial knowledge: visual cues (landmarks), frame of reference (egocentric vs allocentric), spatial orientation, path integration, route knowledge, survey knowledge Internal representation: cognitive map [Golledge, 1999] Other knowledge: external information, previous knowledge and experience Who? Demographics: age and gender Spatial abilities: sense of direction Where? Simulated: virtual environments Real world: indoor and outdoor environments When? Why? How? How much? Before navigation: path planning, search and selection Adapted to overall goal or current task [Holscher, 2011] Sense: proprioception, vision and hearing Cognitive economy During navigation: following a plan, or decision making without a plan Adapted to overall environment or current state Decision and action: brain activity and muscle activation 27 / 31

28 Cognitive models Cognitive models simulate observed human behavior with a computational system or algorithm Application of cognitive models to ARN Build a hierarchical representation of the environment similar to a cognitive map [Kuipers, 2000] Represent spatial knowledge with a graph Shift between navigation strategies using a heuristic Bayesian approach + artificial neural network to learn topological maps and landmarks [Thrun, 1998] Learn abstract representations of the environment and use multiple heuristics to make decisions [Epstein, 2015] Advantages: exploit human knowledge and strategies Disadvantages: No one model has simulated all observed human wayfinding behavior or employed all their strategies Difficult to account for all individual differences among people 28 / 31

29 Outline Background Challenges in ARN Heuristics and metaheuristics Cognitive science Conclusions 29 / 31

30 Conclusions Most approaches have been evaluated in simplified simulated environments that underestimate the hardware challenges of physical robots and reduce computational complexity But people successfully navigate in the real world already so there is room for improvement inspired by human behavior Ultimately, autonomous robot navigation systems must operate in the real world contend with observability, multiagency, dynamism, continuity, and modality issues Future work should address these challenges in a real-world environment Potential future work could create more robust ARN systems by a synergy between metaheuristics and cognitive models 30 / 31

31 Thank You 31 / 31

Metaheuristics and Cognitive Models for. Autonomous Robot Navigation

Metaheuristics and Cognitive Models for. Autonomous Robot Navigation Metaheuristics and Cognitive Models for Autonomous Robot Navigation Raj Korpan raj.m.korpan@gmail.com Department of Computer Science The Graduate Center, CUNY Abstract Although autonomous mobile robots

More information

CEng 713 Evolutionary Computation, Lecture Notes

CEng 713 Evolutionary Computation, Lecture Notes CEng 713 Evolutionary Computation, Lecture Notes Introduction to Evolutionary Computation Evolutionary Computation Elements of Evolution: Reproduction Random variation Competition Selection of contending

More information

Introduction Evolutionary Algorithm Implementation

Introduction Evolutionary Algorithm Implementation Introduction Traditional optimization methods fail when there are complex, nonlinear relationships between the parameters and the value to be optimized, the goal function has many local extrema, and resources

More information

CHAPTER 4 PROPOSED HYBRID INTELLIGENT APPROCH FOR MULTIPROCESSOR SCHEDULING

CHAPTER 4 PROPOSED HYBRID INTELLIGENT APPROCH FOR MULTIPROCESSOR SCHEDULING 79 CHAPTER 4 PROPOSED HYBRID INTELLIGENT APPROCH FOR MULTIPROCESSOR SCHEDULING The present chapter proposes a hybrid intelligent approach (IPSO-AIS) using Improved Particle Swarm Optimization (IPSO) with

More information

What is Evolutionary Computation? Genetic Algorithms. Components of Evolutionary Computing. The Argument. When changes occur...

What is Evolutionary Computation? Genetic Algorithms. Components of Evolutionary Computing. The Argument. When changes occur... What is Evolutionary Computation? Genetic Algorithms Russell & Norvig, Cha. 4.3 An abstraction from the theory of biological evolution that is used to create optimization procedures or methodologies, usually

More information

Metaheuristics. Approximate. Metaheuristics used for. Math programming LP, IP, NLP, DP. Heuristics

Metaheuristics. Approximate. Metaheuristics used for. Math programming LP, IP, NLP, DP. Heuristics Metaheuristics Meta Greek word for upper level methods Heuristics Greek word heuriskein art of discovering new strategies to solve problems. Exact and Approximate methods Exact Math programming LP, IP,

More information

ECTA th International Conference on Evolutionary Computation Theory and Applications

ECTA th International Conference on Evolutionary Computation Theory and Applications ECTA 2019-11th International Conference on Evolutionary Computation Theory and Applications Considered a subfield of computational intelligence focused on combinatorial optimisation problems, evolutionary

More information

Automated Test Case Generation: Metaheuristic Search

Automated Test Case Generation: Metaheuristic Search Automated Test Case Generation: Metaheuristic Search CSCE 747 - Lecture 21-03/29/2016 Testing as a Search Problem Do you have a goal in mind when testing? Can that goal be measured? Then you are searching

More information

Hybrid Model: Overview

Hybrid Model: Overview Hybrid Model: Overview 1990 s saw evolution of architectures labeled reactive planning Developed in response to shortcomings of Reactive approach: Could not deal with problems that require cognitive activities

More information

Genetic Algorithms and Genetic Programming. Lecture 1: Introduction (25/9/09)

Genetic Algorithms and Genetic Programming. Lecture 1: Introduction (25/9/09) Genetic Algorithms and Genetic Programming Michael Herrmann Lecture 1: Introduction (25/9/09) michael.herrmann@ed.ac.uk, phone: 0131 6 517177, Informatics Forum 1.42 Problem Solving at Decreasing Domain

More information

GENETIC ALGORITHMS. Narra Priyanka. K.Naga Sowjanya. Vasavi College of Engineering. Ibrahimbahg,Hyderabad.

GENETIC ALGORITHMS. Narra Priyanka. K.Naga Sowjanya. Vasavi College of Engineering. Ibrahimbahg,Hyderabad. GENETIC ALGORITHMS Narra Priyanka K.Naga Sowjanya Vasavi College of Engineering. Ibrahimbahg,Hyderabad mynameissowji@yahoo.com priyankanarra@yahoo.com Abstract Genetic algorithms are a part of evolutionary

More information

Optimisation and Operations Research

Optimisation and Operations Research Optimisation and Operations Research Lecture 17: Genetic Algorithms and Evolutionary Computing Matthew Roughan http://www.maths.adelaide.edu.au/matthew.roughan/ Lecture_notes/OORII/

More information

What is Genetic Programming(GP)?

What is Genetic Programming(GP)? Agenda What is Genetic Programming? Background/History. Why Genetic Programming? How Genetic Principles are Applied. Examples of Genetic Programs. Future of Genetic Programming. What is Genetic Programming(GP)?

More information

Artificial Bee Colony

Artificial Bee Colony Artificial Bee Colony Prof. Thomas Bäck Artificial Bee Colony 1 Bees and Hive Some of them are social insects like ants, e.g. honey bees Nesting behavior Artificial bee colony algorithm is based on the

More information

IMPLEMENTATION OF AN OPTIMIZATION TECHNIQUE: GENETIC ALGORITHM

IMPLEMENTATION OF AN OPTIMIZATION TECHNIQUE: GENETIC ALGORITHM IMPLEMENTATION OF AN OPTIMIZATION TECHNIQUE: GENETIC ALGORITHM TWINKLE GUPTA* Department of Computer Science, Hindu Kanya MahaVidyalya, Jind, India Abstract We are encountered with various optimization

More information

A MODEIFED ARTIFICIAL BEE COLONY ALGORITHM FOR GENE SELECTION IN CLASSIFYING CANCER

A MODEIFED ARTIFICIAL BEE COLONY ALGORITHM FOR GENE SELECTION IN CLASSIFYING CANCER M.Sc. Engg. Thesis A MODEIFED ARTIFICIAL BEE COLONY ALGORITHM FOR GENE SELECTION IN CLASSIFYING CANCER by Johra Muhammad Moosa Submitted to Department of Computer Science and Engineering in partial fulfillment

More information

CHAPTER 3 RESEARCH METHODOLOGY

CHAPTER 3 RESEARCH METHODOLOGY 72 CHAPTER 3 RESEARCH METHODOLOGY Inventory management is considered to be an important field in Supply chain management. Once the efficient and effective management of inventory is carried out throughout

More information

The Metaphor. Individuals living in that environment Individual s degree of adaptation to its surrounding environment

The Metaphor. Individuals living in that environment Individual s degree of adaptation to its surrounding environment Genetic Algorithms Sesi 14 Optimization Techniques Mathematical Programming Network Analysis Branch & Bound Simulated Annealing Tabu Search Classes of Search Techniques Calculus Base Techniqes Fibonacci

More information

Mrs. Shahana Gajala Qureshi 1, Mrs. Uzma Arshi Ansari 2

Mrs. Shahana Gajala Qureshi 1, Mrs. Uzma Arshi Ansari 2 IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 2278-0661 Volume 4, Issue 1 (Sep-Oct. 2012), PP 06-13 An efficient and powerful advanced algorithm for solving real coded numerical optimization problem:

More information

Foundations of Artificial Intelligence

Foundations of Artificial Intelligence Foundations of Artificial Intelligence 21. Combinatorial Optimization: Advanced Techniques Malte Helmert University of Basel April 9, 2018 Combinatorial Optimization: Overview Chapter overview: combinatorial

More information

Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing Scientific Journal of Informatics Vol. 3, No. 2, November 216 p-issn 247-7658 http://journal.unnes.ac.id/nju/index.php/sji e-issn 246-4 Comparison Performance of Genetic Algorithm and Ant Colony Optimization

More information

Comp215: Genetic Algorithms - Part 1

Comp215: Genetic Algorithms - Part 1 Comp215: Genetic Algorithms - Part 1 Mack Joyner, Dan S. Wallach (Rice University) Copyright 2016, Mack Joyner, Dan S. Wallach. All rights reserved. Darwin s Theory of Evolution Individual organisms differ

More information

EMM4131 Popülasyon Temelli Algoritmalar (Population-based Algorithms) Introduction to Meta-heuristics and Evolutionary Algorithms

EMM4131 Popülasyon Temelli Algoritmalar (Population-based Algorithms) Introduction to Meta-heuristics and Evolutionary Algorithms 2017-2018 Güz Yarıyılı Balikesir Universitesi, Endustri Muhendisligi Bolumu EMM4131 Popülasyon Temelli Algoritmalar (Population-based Algorithms) 2 Introduction to Meta-heuristics and Evolutionary Algorithms

More information

Genetic Algorithm and Application in training Multilayer Perceptron Model

Genetic Algorithm and Application in training Multilayer Perceptron Model Genetic Algorithm and Application in training Multilayer Perceptron Model Tuan Dung Lai Faculty of Science, Engineering and Technology Swinburne University of Technology Hawthorn, Victoria 3122 Email:

More information

A New Methodology for Solving Different Economic Dispatch Problems

A New Methodology for Solving Different Economic Dispatch Problems A New Methodology for Solving Different Economic Dispatch Problems Divya Mathur Assistant Professor, JECRC University, Jaipur Abstract- This paper presents a Biogeography-Based Optimization (BBO) algorithm

More information

SWARM-BASED OPTIMISATION

SWARM-BASED OPTIMISATION KVALITA INOVÁCIA PROSPERITA / QUALITY INNOVATION PROSPERITY XIII/1 2009 45 SWARM-BASED OPTIMISATION LENKA RAUDENSKÁ 1 INTRODUCTION Swarm intelligence is based on nature-inspired behaviour and is successfully

More information

A Process Oriented Modeling Concept for Rich Vehicle Routing Problems

A Process Oriented Modeling Concept for Rich Vehicle Routing Problems Collaborative Research Center SFB559 Modeling of Large Logistic Networks Computer Science Algorithm Engineering A Process Oriented Modeling Concept for Rich Vehicle Routing Problems Andreas Reinholz VIP

More information

Relative Study of CGS with ACO and BCO Swarm Intelligence Techniques

Relative Study of CGS with ACO and BCO Swarm Intelligence Techniques Relative Study of CGS with ACO and BCO Swarm Intelligence Techniques Abstract Ms. T.Hashni 1 Ms.T.Amudha 2 Assistant Professor 1, 2 P.S.G.R.Krishnammal College for Women 1, Bharathiar University 2 Coimbatore,

More information

GENETIC ALGORITHM BASED APPROACH FOR THE SELECTION OF PROJECTS IN PUBLIC R&D INSTITUTIONS

GENETIC ALGORITHM BASED APPROACH FOR THE SELECTION OF PROJECTS IN PUBLIC R&D INSTITUTIONS GENETIC ALGORITHM BASED APPROACH FOR THE SELECTION OF PROJECTS IN PUBLIC R&D INSTITUTIONS SANJAY S, PRADEEP S, MANIKANTA V, KUMARA S.S, HARSHA P Department of Human Resource Development CSIR-Central Food

More information

Use of Genetic Algorithms in Discrete Optimalization Problems

Use of Genetic Algorithms in Discrete Optimalization Problems Use of Genetic Algorithms in Discrete Optimalization Problems Alena Rybičková supervisor: Ing. Denisa Mocková PhD Faculty of Transportation Sciences Main goals: design of genetic algorithm for vehicle

More information

ABSTRACT COMPUTER EVOLUTION OF GENE CIRCUITS FOR CELL- EMBEDDED COMPUTATION, BIOTECHNOLOGY AND AS A MODEL FOR EVOLUTIONARY COMPUTATION

ABSTRACT COMPUTER EVOLUTION OF GENE CIRCUITS FOR CELL- EMBEDDED COMPUTATION, BIOTECHNOLOGY AND AS A MODEL FOR EVOLUTIONARY COMPUTATION ABSTRACT COMPUTER EVOLUTION OF GENE CIRCUITS FOR CELL- EMBEDDED COMPUTATION, BIOTECHNOLOGY AND AS A MODEL FOR EVOLUTIONARY COMPUTATION by Tommaso F. Bersano-Begey Chair: John H. Holland This dissertation

More information

Bio-inspired Active Vision. Martin Peniak, Ron Babich, John Tran and Davide Marocco

Bio-inspired Active Vision. Martin Peniak, Ron Babich, John Tran and Davide Marocco Bio-inspired Active Vision Martin Peniak, Ron Babich, John Tran and Davide Marocco GPU Computing Lab Traditional Computer Vision 3 Traditional Computer Vision Teaching a computer to classify objects has

More information

Selecting Quality Initial Random Seed For Metaheuristic Approaches: A Case Of Timetabling Problem

Selecting Quality Initial Random Seed For Metaheuristic Approaches: A Case Of Timetabling Problem Abu Bakar Md Sultan, Ramlan Mahmod, Md Nasir Sulaiman, and Mohd Rizam Abu Bakar Selecting Quality Initial Random Seed For Metaheuristic Approaches: A Case Of tabling Problem 1 Abu Bakar Md Sultan, 2 Ramlan

More information

In collaboration with Jean-Yves Lucas (EDF)

In collaboration with Jean-Yves Lucas (EDF) In collaboration with Jean-Yves Lucas (EDF) Table of Contents 1. Introduction 2. Related works 3. Mathematical Model 4. Multi-objective Evolutionary Algorithm 5. Input Data & Experimental results 6. Conclusions

More information

An Evolutionary Approach involving Training of ANFIS with the help of Genetic Algorithm for PID Controller Tuning

An Evolutionary Approach involving Training of ANFIS with the help of Genetic Algorithm for PID Controller Tuning An Evolutionary Approach involving Training of ANFIS with the help of Genetic Algorithm for PID... An Evolutionary Approach involving Training of ANFIS with the help of Genetic Algorithm for PID Controller

More information

Design and Development of Enhanced Optimization Techniques based on Ant Colony Systems

Design and Development of Enhanced Optimization Techniques based on Ant Colony Systems IJIRST International Journal for Innovative Research in Science & Technology Volume 3 Issue 04 September 2016 ISSN (online): 2349-6010 Design and Development of Enhanced Optimization Techniques based on

More information

A Survey on Bio-Inspired Computing Algorithms and Application

A Survey on Bio-Inspired Computing Algorithms and Application IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727 PP 51-56 www.iosrjournals.org A Survey on Bio-Inspired Computing Algorithms and Application Pavan Ughade 1 Aishwarya

More information

College of information technology Department of software

College of information technology Department of software University of Babylon Undergraduate: third class College of information technology Department of software Subj.: Application of AI lecture notes/2011-2012 ***************************************************************************

More information

Evolutionary Algorithms - Population management and popular algorithms Kai Olav Ellefsen

Evolutionary Algorithms - Population management and popular algorithms Kai Olav Ellefsen INF3490 - Biologically inspired computing Lecture 3: Eiben and Smith, chapter 5-6 Evolutionary Algorithms - Population management and popular algorithms Kai Olav Ellefsen Repetition: General scheme of

More information

A Survey on Various Task Scheduling Algorithm in cloud Environment

A Survey on Various Task Scheduling Algorithm in cloud Environment A Survey on Various Task Scheduling Algorithm in cloud Environment Gurjeet kaur [1], Gurjot singh sodhi [2] Shaheed Udham Singh College of Engineering & Technology,(Tangori) Abstract - Cloud computing

More information

In order to have GA, you must have a way to rate a given solution (fitness function). The fitness function must be continuous.

In order to have GA, you must have a way to rate a given solution (fitness function). The fitness function must be continuous. Disclaimer This document is a summary of Prof. Floreano s Bio-inspired Adaptive Machines course. The purpose is to help the student revise for the oral examination. This document should not be considered

More information

Computational Intelligence Lecture 20:Intorcution to Genetic Algorithm

Computational Intelligence Lecture 20:Intorcution to Genetic Algorithm Computational Intelligence Lecture 20:Intorcution to Genetic Algorithm Farzaneh Abdollahi Department of Electrical Engineering Amirkabir University of Technology Fall 2012 Farzaneh Abdollahi Computational

More information

Load Disaggregation with Metaheuristic Optimization

Load Disaggregation with Metaheuristic Optimization Load Disaggregation with Metaheuristic Optimization Dominik Egarter and Wilfried Elmenreich Institute of Networked and Embedded Systems / Lakeside Labs Alpen-Adria-Universität Klagenfurt, Austria dominik.egarter@aau.at,

More information

COMPUTATIONAL INTELLIGENCE FOR SUPPLY CHAIN MANAGEMENT AND DESIGN: ADVANCED METHODS

COMPUTATIONAL INTELLIGENCE FOR SUPPLY CHAIN MANAGEMENT AND DESIGN: ADVANCED METHODS COMPUTATIONAL INTELLIGENCE FOR SUPPLY CHAIN MANAGEMENT AND DESIGN: ADVANCED METHODS EDITED BOOK IGI Global (former IDEA publishing) Book Editors: I. Minis, V. Zeimpekis, G. Dounias, N. Ampazis Department

More information

Improvement and Implementation of Best-worst Ant Colony Algorithm

Improvement and Implementation of Best-worst Ant Colony Algorithm Research Journal of Applied Sciences, Engineering and Technology 5(21): 4971-4976, 2013 ISSN: 2040-7459; e-issn: 2040-7467 Maxwell Scientific Organization, 2013 Submitted: July 31, 2012 Accepted: September

More information

VISHVESHWARAIAH TECHNOLOGICAL UNIVERSITY S.D.M COLLEGE OF ENGINEERING AND TECHNOLOGY. A seminar report on GENETIC ALGORITHMS.

VISHVESHWARAIAH TECHNOLOGICAL UNIVERSITY S.D.M COLLEGE OF ENGINEERING AND TECHNOLOGY. A seminar report on GENETIC ALGORITHMS. VISHVESHWARAIAH TECHNOLOGICAL UNIVERSITY S.D.M COLLEGE OF ENGINEERING AND TECHNOLOGY A seminar report on GENETIC ALGORITHMS Submitted by Pranesh S S 2SD06CS061 8 th semester DEPARTMENT OF COMPUTER SCIENCE

More information

Path-finding in Multi-Agent, unexplored And Dynamic Military Environment Using Genetic Algorithm

Path-finding in Multi-Agent, unexplored And Dynamic Military Environment Using Genetic Algorithm International Journal of Computer Networks and Communications Security VOL. 2, NO. 9, SEPTEMBER 2014, 285 291 Available online at: www.ijcncs.org ISSN 2308-9830 C N C S Path-finding in Multi-Agent, unexplored

More information

Ant Colony Optimisation

Ant Colony Optimisation Ant Colony Optimisation Alexander Mathews, Angeline Honggowarsito & Perry Brown 1 Image Source: http://baynature.org/articles/the-ants-go-marching-one-by-one/ Contents Introduction to Ant Colony Optimisation

More information

Software Next Release Planning Approach through Exact Optimization

Software Next Release Planning Approach through Exact Optimization Software Next Release Planning Approach through Optimization Fabrício G. Freitas, Daniel P. Coutinho, Jerffeson T. Souza Optimization in Software Engineering Group (GOES) Natural and Intelligent Computation

More information

Genetic Algorithm: An Optimization Technique Concept

Genetic Algorithm: An Optimization Technique Concept Genetic Algorithm: An Optimization Technique Concept 1 Uma Anand, 2 Chain Singh 1 Student M.Tech (3 rd sem) Department of Computer Science Engineering Dronacharya College of Engineering, Gurgaon-123506,

More information

PARALLEL LINE AND MACHINE JOB SCHEDULING USING GENETIC ALGORITHM

PARALLEL LINE AND MACHINE JOB SCHEDULING USING GENETIC ALGORITHM PARALLEL LINE AND MACHINE JOB SCHEDULING USING GENETIC ALGORITHM Dr.V.Selvi Assistant Professor, Department of Computer Science Mother Teresa women s University Kodaikanal. Tamilnadu,India. Abstract -

More information

Energy management using genetic algorithms

Energy management using genetic algorithms Energy management using genetic algorithms F. Garzia, F. Fiamingo & G. M. Veca Department of Electrical Engineering, University of Rome "La Sapienza", Italy Abstract An energy management technique based

More information

Computational algorithms inspired by biological processes and evolution

Computational algorithms inspired by biological processes and evolution Computational algorithms inspired by biological processes and evolution M. Janga Reddy and D. Nagesh Kumar* In recent times computational algorithms inspired by biological processes and evolution are gaining

More information

Introduction to Artificial Intelligence. Prof. Inkyu Moon Dept. of Robotics Engineering, DGIST

Introduction to Artificial Intelligence. Prof. Inkyu Moon Dept. of Robotics Engineering, DGIST Introduction to Artificial Intelligence Prof. Inkyu Moon Dept. of Robotics Engineering, DGIST Chapter 9 Evolutionary Computation Introduction Intelligence can be defined as the capability of a system to

More information

AUV Search Target Research Based meta-heuristic algorithm

AUV Search Target Research Based meta-heuristic algorithm , pp.22-26 http://dx.doi.org/0.4257/astl.24.79.05 AUV Search Target Research Based meta-heuristic algorithm JianJun LI,3, RuBo ZHANG,2, Yu YANG 3 (. College of Computer Science and Technology, Harbin Engineering

More information

Workshop on Particle Swarm Optimization and Evolutionary Computation (20-21 February 2018)

Workshop on Particle Swarm Optimization and Evolutionary Computation (20-21 February 2018) Table of Contents Tutorial: An introduction to nature-inspired metaheuristic algorithms 2 A multiobjective memetic algorithm based on particle swarm optimization 3 A novel discrete particle swarm optimization

More information

Optimizing Dynamic Flexible Job Shop Scheduling Problem Based on Genetic Algorithm

Optimizing Dynamic Flexible Job Shop Scheduling Problem Based on Genetic Algorithm International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347 5161 2017 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Optimizing

More information

A Genetic Algorithm on Inventory Routing Problem

A Genetic Algorithm on Inventory Routing Problem A Genetic Algorithm on Inventory Routing Problem Artvin Çoruh University e-mail: nevin.aydin@gmail.com Volume 3 No 3 (2014) ISSN 2158-8708 (online) DOI 10.5195/emaj.2014.31 http://emaj.pitt.edu Abstract

More information

Enhancement of Quadrotor Positioning Using EKF-SLAM

Enhancement of Quadrotor Positioning Using EKF-SLAM , pp.56-60 http://dx.doi.org/10.14257/astl.2015.106.13 Enhancement of Quadrotor Positioning Using EKF-SLAM Jae-young Hwang 1,1 and Young-wan Cho 1 1 Dept. of Computer Engineering, Seokyeong University

More information

Evolutionary Computation

Evolutionary Computation Evolutionary Computation Evolution and Intelligent Besides learning ability, intelligence can also be defined as the capability of a system to adapt its behaviour to ever changing environment. Evolutionary

More information

ANT COLONY ALGORITHM APPLIED TO FUNDAMENTAL FREQUENCY MAXIMIZATION OF LAMINATED COMPOSITE CYLINDRICAL SHELLS

ANT COLONY ALGORITHM APPLIED TO FUNDAMENTAL FREQUENCY MAXIMIZATION OF LAMINATED COMPOSITE CYLINDRICAL SHELLS ANT COLONY ALGORITHM APPLIED TO FUNDAMENTAL FREQUENCY MAXIMIZATION OF LAMINATED COMPOSITE CYLINDRICAL SHELLS Rubem M. Koide 1 *, Marco A. Luersen 1 ** 1 Laboratório de Mecânica Estrutural (LaMEs), Universidade

More information

AN OVERVIEW OF THE SCHEDULING POLICIES AND ALGORITHMS IN GRID COMPUTING

AN OVERVIEW OF THE SCHEDULING POLICIES AND ALGORITHMS IN GRID COMPUTING AN OVERVIEW OF THE SCHEDULING POLICIES AND ALGORITHMS IN GRID COMPUTING D.I. George Amalarethinam, Director-MCA & Associate Professor of Computer Science, Jamal Mohamed College (Autonomous), Tiruchirappalli,

More information

Genetic Algorithms and Genetic Programming Lecture 13

Genetic Algorithms and Genetic Programming Lecture 13 Genetic Algorithms and Genetic Programming Lecture 13 Gillian Hayes 10th November 2008 Pragmatics of GA Design 1 Selection methods Crossover Mutation Population model and elitism Spatial separation Maintaining

More information

Ant Systems of Optimization: Introduction and Review of Applications

Ant Systems of Optimization: Introduction and Review of Applications International Journal of Electronics Engineering Research. ISSN 0975-6450 Volume 9, Number 2 (2017) pp. 275-279 Research India Publications http://www.ripublication.com Ant Systems of Optimization: Introduction

More information

Application of a Genetic Algorithm to improve an existing solution for the. General Assignment Problem.

Application of a Genetic Algorithm to improve an existing solution for the. General Assignment Problem. Application of a Genetic Algorithm to improve an existing solution for the General Assignment Problem. Paul Juell Amal S. Perera Kendall E. Nygard Department of Computer Science North Dakota State University

More information

Evolutionary Algorithms

Evolutionary Algorithms Evolutionary Algorithms Evolutionary Algorithms What is Evolutionary Algorithms (EAs)? Evolutionary algorithms are iterative and stochastic search methods that mimic the natural biological evolution and/or

More information

CapSel GA Genetic Algorithms.

CapSel GA Genetic Algorithms. CapSel GA - 01 Genetic Algorithms keppens@rijnh.nl Typical usage: optimization problems both minimization and maximization of complicated functions completely standard problem with non-standard solution

More information

Improving Differential Evolution Algorithm with Activation Strategy

Improving Differential Evolution Algorithm with Activation Strategy 2009 International Conference on Machine Learning and Computing IPCSIT vol.3 (2011) (2011) IACSIT Press, Singapore Improving Differential Evolution Algorithm with Activation Strategy Zhan-Rong Hsu 1, Wei-Ping

More information

CHAPTER 2 REACTIVE POWER OPTIMIZATION A REVIEW

CHAPTER 2 REACTIVE POWER OPTIMIZATION A REVIEW 14 CHAPTER 2 REACTIVE POWER OPTIMIZATION A REVIEW 2.1 INTRODUCTION Reactive power optimization is an important function both in planning for the future and day-to-day operations of power systems. It uses

More information

A TABU SEARCH METAHEURISTIC FOR ASSIGNMENT OF FLOATING CRANES

A TABU SEARCH METAHEURISTIC FOR ASSIGNMENT OF FLOATING CRANES 1 st Logistics International Conference Belgrade, Serbia 28 - November 13 A TABU SEARCH METAHEURISTIC FOR ASSIGNMENT OF FLOATING CRANES Dragana M. Drenovac * University of Belgrade, Faculty of Transport

More information

Chapter 6 Evolutionary Computation and Evolving Connectionist Systems

Chapter 6 Evolutionary Computation and Evolving Connectionist Systems Chapter 6 Evolutionary Computation and Evolving Connectionist Systems Prof. Nik Kasabov nkasabov@aut.ac.nz http://www.kedri.info 12/16/2002 Nik Kasabov - Evolving Connectionist Systems Overview Evolutionary

More information

A systematic review of bio-inspired service concretization

A systematic review of bio-inspired service concretization University of Wollongong Research Online Faculty of Engineering and Information Sciences - Papers: Part A Faculty of Engineering and Information Sciences 2014 A systematic review of bio-inspired service

More information

Logistics. Final exam date. Project Presentation. Plan for this week. Evolutionary Algorithms. Crossover and Mutation

Logistics. Final exam date. Project Presentation. Plan for this week. Evolutionary Algorithms. Crossover and Mutation Logistics Crossover and Mutation Assignments Checkpoint -- Problem Graded -- comments on mycourses Checkpoint --Framework Mostly all graded -- comments on mycourses Checkpoint -- Genotype / Phenotype Due

More information

A Study of Crossover Operators for Genetic Algorithms to Solve VRP and its Variants and New Sinusoidal Motion Crossover Operator

A Study of Crossover Operators for Genetic Algorithms to Solve VRP and its Variants and New Sinusoidal Motion Crossover Operator International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 13, Number 7 (2017), pp. 1717-1733 Research India Publications http://www.ripublication.com A Study of Crossover Operators

More information

An Improved Immune Genetic Algorithm for Capacitated Vehicle Routing Problem

An Improved Immune Genetic Algorithm for Capacitated Vehicle Routing Problem Send Orders for Reprints to reprints@benthamscience.ae 560 The Open Cybernetics & Systemics Journal, 2014, 8, 560-565 Open Access An Improved Immune Genetic Algorithm for Capacitated Vehicle Routing Problem

More information

Cloud Load Balancing Based on ACO Algorithm

Cloud Load Balancing Based on ACO Algorithm Cloud Load Balancing Based on ACO Algorithm Avtar Singh, Kamlesh Dutta, Himanshu Gupta Department of Computer Science, National Institute of Technology,Hamirpur, HP, India Department of Computer Science,

More information

Advanced Metaheuristics. Daniele Vigo D.E.I. - Università di Bologna

Advanced Metaheuristics. Daniele Vigo D.E.I. - Università di Bologna Advanced Metaheuristics Daniele Vigo D.E.I. - Università di Bologna daniele.vigo@unibo.it Main families of Metaheuristics Single-solution methods Basic: Tabu Search, Simulated Annealing Advanced: Iterated

More information

TOLERANCE ALLOCATION OF MECHANICAL ASSEMBLIES USING PARTICLE SWARM OPTIMIZATION

TOLERANCE ALLOCATION OF MECHANICAL ASSEMBLIES USING PARTICLE SWARM OPTIMIZATION 115 Chapter 6 TOLERANCE ALLOCATION OF MECHANICAL ASSEMBLIES USING PARTICLE SWARM OPTIMIZATION This chapter discusses the applicability of another evolutionary algorithm, named particle swarm optimization

More information

Research and Solution for the Shortest Routing Problem of Logistics Vehicle Based on Genetic Algorithm

Research and Solution for the Shortest Routing Problem of Logistics Vehicle Based on Genetic Algorithm doi:10.21311/002.31.6.14 Research and Solution for the Shortest Routing Problem of Logistics Vehicle Based on Genetic Algorithm Haifeng Hu, Xiaohui Wang* College of Information Technology, Pingdingshan

More information

Contents. Preface...VII

Contents. Preface...VII Contents Preface...VII 1 An Overview of Evolutionary Computation... 1 1.1 Examples of Evolutionary Computation... 3 1.1.1 Predators Running Backward... 3 1.1.2 Wood-Burning Stoves... 5 1.1.3 Hyperspectral

More information

GENETIC ALGORITHM A NOBLE APPROACH FOR ECONOMIC LOAD DISPATCH

GENETIC ALGORITHM A NOBLE APPROACH FOR ECONOMIC LOAD DISPATCH International Journal of Engineering Research and Applications (IJERA) ISSN: 48-96 National Conference on Emerging Trends in Engineering & Technology (VNCET-30 Mar 1) GENETIC ALGORITHM A NOBLE APPROACH

More information

Recapitulation of Ant Colony and Firefly Optimization Techniques

Recapitulation of Ant Colony and Firefly Optimization Techniques International Journal of Bioinformatics and Biomedical Engineering Vol. 1, No. 2, 2015, pp. 175-180 http://www.aiscience.org/journal/ijbbe Recapitulation of Ant Colony and Firefly Optimization Techniques

More information

PARALLELIZATION OF HYBRID SIMULATED ANNEALING AND GENETIC ALGORITHM FOR SHORT-TERM PRODUCTION SCHEDULING

PARALLELIZATION OF HYBRID SIMULATED ANNEALING AND GENETIC ALGORITHM FOR SHORT-TERM PRODUCTION SCHEDULING PARALLELIZATION OF HYBRID SIMULATED ANNEALING AND GENETIC ALGORITHM FOR SHORT-TERM PRODUCTION SCHEDULING Karl Kurbel # Bernd Schneider # Kirti Singh + # Institute of Business Informatics, University of

More information

Incorporating Psychology Model of Emotion into Ant Colony Optimization Algorithm

Incorporating Psychology Model of Emotion into Ant Colony Optimization Algorithm 12th WSEAS Int. Conf. on APPLIED MATHEMATICS Cairo Egypt December 29-31 2007 222 Incorporating Psychology Model of Emotion into Ant Colony Optimization Algorithm Jiann-Horng Lin Department of Information

More information

CVEN 5393 April 1, 2013

CVEN 5393 April 1, 2013 CVEN 5393 April 1, 2013 Topics Revisit multi-objective optimization Heuristics and Metaheuristics a conceptual overview Multi-criteria decision analysis Multiobjective Optimization Topics Multiple objectives

More information

10. Lecture Stochastic Optimization

10. Lecture Stochastic Optimization Soft Control (AT 3, RMA) 10. Lecture Stochastic Optimization Genetic Algorithms 10. Structure of the lecture 1. Soft control: the definition and limitations, basics of epert" systems 2. Knowledge representation

More information

A HYBRID GENETIC ALGORITHM FOR JOB SHOP SCHEUDULING

A HYBRID GENETIC ALGORITHM FOR JOB SHOP SCHEUDULING A HYBRID GENETIC ALGORITHM FOR JOB SHOP SCHEUDULING PROF. SARVADE KISHORI D. Computer Science and Engineering,SVERI S College Of Engineering Pandharpur,Pandharpur,India KALSHETTY Y.R. Assistant Professor

More information

9/17/14 LOCAL SEARCH. Today. Reading. Read AIMA Ch Objectives. Simulated Annealing Genetic algorithms Gradient ascent

9/17/14 LOCAL SEARCH. Today. Reading. Read AIMA Ch Objectives. Simulated Annealing Genetic algorithms Gradient ascent LOCAL SEARCH Today Reading Read AIMA Ch. 4.1-4.2 Objectives Simulated Annealing Genetic algorithms Gradient ascent 1 Recall the N-Queens problem incremental formulation N-Queens alternative approach complete

More information

Simulation approaches for optimization in business and service systems

Simulation approaches for optimization in business and service systems Simulation approaches for optimization in business and service systems Imed Kacem kacem@univ-metz.fr Professor - Université Paul Verlaine Metz http://kacem.imed.perso.neuf.fr/site/ FUBUTEC 2, Future Business

More information

Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming

Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming Su Nguyen 1, Mengjie Zhang 1, Mark Johnston 2, Kay Chen Tan 3 1 Victoria

More information

Genetic algorithms and code optimization. A quiet revolution

Genetic algorithms and code optimization. A quiet revolution Genetic algorithms and code optimization Devika Subramanian Rice University Work supported by DARPA and the USAF Research Labs A quiet revolution (May 1997) Deep Blue vs Kasparaov first match won against

More information

Optimum Design of Water Conveyance System by Ant Colony Optimization Algorithms

Optimum Design of Water Conveyance System by Ant Colony Optimization Algorithms Optimum Design of Water Conveyance System by Ant Colony Optimization Algorithms HABIBEH ABBASI, ABBAS AFSHAR, MOHAMMAD REZA JALALI Department of Civil Engineering Iran University of Science and Technology

More information

BIO INSPIRED COMPUTING

BIO INSPIRED COMPUTING BIO INSPIRED COMPUTING SEMINAR REPORT Submitted by ULLAS MOHAN In partial fulfillment of the award of the degree Of BACHELOR OF TECHNOLOGY In COMPUTER SCIENCE AND ENGINEERING SCHOOL OF ENGINEERING COCHIN

More information

Agent-Based Simulation of Cooperative Hunting with UAVs

Agent-Based Simulation of Cooperative Hunting with UAVs McCune, R. Ryan, and Gregory R. Madey. "Agent-based simulation of cooperative hunting with UAVs." Proceedings of the Agent-Directed Simulation Symposium. Society for Computer Simulation International,

More information

DM811 (5 ECTS - 1st Quarter) Heuristics for Combinatorial Optimization Heuristikker og lokalsøgningsalgoritmer for kombinatorisk optimering

DM811 (5 ECTS - 1st Quarter) Heuristics for Combinatorial Optimization Heuristikker og lokalsøgningsalgoritmer for kombinatorisk optimering DM811 (5 ECTS - 1st Quarter) Heuristics for Combinatorial Optimization Heuristikker og lokalsøgningsalgoritmer for kombinatorisk optimering DM812 (5 ECTS - 2nd Quarter) Metaheuristics Metaheuristikker

More information

Procedia - Social and Behavioral Sciences 109 ( 2014 ) Selection and peer review under responsibility of Organizing Committee of BEM 2013.

Procedia - Social and Behavioral Sciences 109 ( 2014 ) Selection and peer review under responsibility of Organizing Committee of BEM 2013. Available online at www.sciencedirect.com ScienceDirect Procedia - Social and Behavioral Sciences 109 ( 2014 ) 779 783 2 nd World Conference On Business, Economics And Management-WCBEM 2013 A hybrid metaheuristic

More information

REAL-world situations, such as those arising in economics. A Survey on Modeling and Optimizing Multi-Objective Systems

REAL-world situations, such as those arising in economics. A Survey on Modeling and Optimizing Multi-Objective Systems IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, VOL. X, NO. X, 2016 1 A Survey on Modeling and Optimizing Multi-Objective Systems Jin-Hee Cho, Senior Member, IEEE, Yating Wang, Ing-Ray Chen, Member, IEEE, Kevin

More information

Solving Multi-Objective Multi-Constraint Optimization Problems using Hybrid Ants System and Tabu Search

Solving Multi-Objective Multi-Constraint Optimization Problems using Hybrid Ants System and Tabu Search MIC2003: The Fifth Metaheuristics International Conference HASTS-1 Solving Multi-Objective Multi-Constraint Optimization Problems using Hybrid Ants System and Tabu Search Hoong Chuin LAU, Min Kwang LIM,

More information

Application of Robotics and AI Technologies to the Future ATM

Application of Robotics and AI Technologies to the Future ATM Application of Robotics and AI Technologies to the Future ATM Anibal Ollero University of Seville http://grvc.us.es aollero@us.es Scientific Advisor of CATEC Aerospace Technologies Center http://www.catec.aeo

More information

NAVAL POSTGRADUATE SCHOOL THESIS

NAVAL POSTGRADUATE SCHOOL THESIS NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS SWARM INTELLIGENCE FOR AUTONOMOUS UAV CONTROL by Natalie R. Frantz June 2005 Thesis Advisor: Second Reader: Phillip E. Pace David C. Jenn Approved

More information