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Agenda
1. Project Team
2. Project Requirements
3. Proposal
4. Project Management
5. Project Product
6. Project Demo
7. Q&A Session University of Wollongong, Australia
Project Team
KHO PUAY MENG 3059571
KOH MENG HONG 3059686
NG CHIOU YOONG 3064827
YIP CHEW HONG 3059546
University of Wollongong, Australia
Project Requirements
Project Title:
“NEST (or HIVE: A simulator of life in and around an ant (bee) nest (hive). (Group of 3 to 4 Students) “~ Quoted
University of Wollongong, Australia
Project Requirements
OBJECTIVE:
“The group should choose a colony creature; probably either an ant or a bee, and model the nest or hive life. This should include aspects such as building, foraging and patrolling, where such behaviours are typical. Specific species habits, such as bee swarming should also be modelled. Interactions with their physical environment will need to be considered too, including such things as the effect of rain and other weather. There will need to be a fair amount of research into behaviour patterns of the chosen creature. There are many different species of ants, and allowing flexibility for behaviours differing between species would be useful, and shouldn’t be too difficult. The expectation is to provide a graphical simulation, although some useful textual reports should also be provided by the software. “ ~ Quoted
University of Wollongong, Australia
Proposal
Study Subject: Colony Creature: Honey Bee Literature Review & Research on Honeybee
University of Wollongong, Australia
Proposal
Solution: Model Honeybee population growth in dynamic
data Provide Graphical Simulation on Bee behaviors
such as: Building Scouting Foraging Patrolling Swarming Attack behaviors
Project Management
Schedule Methodology Tools Documentation Delivery
University of Wollongong, Australia
Methodology
Dynamic System Development Method (DSDM) Define as a framework for an iterative and
incremental approach to the development of Information Systems.
Timeboxing Schedule for Reviews
University of Wollongong, Australia
DSDM
Why use DSDM to manage Team? Active User Involvement Development is iterative, driven by user
feedback All changes are reversible Testing throughout life cycle
Tools
Communication Media: Teleconference using Skype, Netmeeting Chat online with MSN, Yahoo, GoogleTalk WebMail on Hotmail, Gmail, Yahoo Mail Feedback via Project Forum Update status via Project Website
University of Wollongong, Australia
Documentation
Type of documents
Versioning Format
Revision Procedure
University of Wollongong, Australia
Documentation
Type of Documents include: Initial Submission:
Project Proposal (CR) Project Schedule (CR)
Fortnightly Submission: Project Diary (CR)
Final Submission: Final Report Technical Report (CR) Test Report (CR) User Manual (CR)
University of Wollongong, Australia
Documentation
Change Management Control Procedure
Initial Version
Edit Document
Document Update?
Version X change
University of Wollongong, Australia
Delivery
Documentation Product (Software Application)
MPEG Video Installation CD Source Code User Manual*
University of Wollongong, Australia
Project Product
Product Overview Genetic Algorithm (GA)
Defintion Implementation Applications
University of Wollongong, Australia
Product Overview
Product Name: Bee Hive Simulator Period complete: 4mths Version: 1.0.0
Purpose: This software is a simulator on Honeybees’ network life cycle. This simulator will include aspects, such as building, scouting, foraging and patrolling. It may also include Honeybees' behavior and habits, such as bee swarming, and how honeybees interact with their physical environment, e.g. the effect of pesticides.
The goal is to provide a graphical simulation, with some useful textual reports which it will be help to illustrate honeybee life cycle.
University of Wollongong, Australia
Product Overview
Main Features: Graphical Simulation Genetic Algorithms Technique (GA) Generate Report
Product Overview
Target Audience Researchers Students Bee Farmers
University of Wollongong, Australia
Genetic Algorithm
What is Genetic Algorithm? In short, it is called GA A search technique used in computer science to
find approximate solutions to optimization and search problems.
University of Wollongong, Australia
Genetic Algorithm
How is GA implemented? Problem Modeling
Using Chromosomes and Genes to represent Food Source
Selecting Best Food Source Combination Base on Highest Fitness Value
E.g
Food Index : 3
Water Index : 1
Chromosome A
Genetic Algorithm
Fitness Function Assign and evaluate chromosome fitness value Based on defined constraints
Food Quality (Sugar Lvl > 30%) Food Availability (Nectar & Pollen Quantity) Food Range Obstacles
Genetic Algorithm
Mutation
Food Index : 1
Water Index : 1
Food Index : 1
Water Index : 3
Chromosome A
Chromosome A
Before Mutation
After Mutation
Genetic Algorithm
Crossover
Food Index : 1
Water Index : 3
Parent A
Before Crossover
Food Index : 2
Water Index : 4
Parent B
After Crossover
Food Index : 1
Water Index : 4
Offspring A
Food Index : 2
Water Index : 3
Offspring B
Applications
Foraging Understand routing behaviors in the network using GA
By changing the constraints used in the fitness function, we can obtain the best routing routes to a certain specified destination
Constraints to be consider:- Routing Distance Router’s Capacity Bandwidth of Network File Size
Future Enhancement
Dynamic Selection Of Best Food Source Simulating Bees Behavior Under Extreme
Weather Condition (Below 8 Degrees Celsius)
Graphical Statistic Report (E.g Lines Graph or Bar Chart)
And more…