PROJECT LM-01 Presentation 16 Oct 2006 University of Wollongong, Australia

PROJECT LM-01

Presentation

16 Oct 2006 University of Wollongong, Australia

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


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


Proposal

Study Subject: Colony Creature: Honey Bee Literature Review & Research on Honeybee


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

Bee Hive Simulator


Project Management

Schedule Methodology Tools Documentation Delivery


Schedule

Total duration: <5 months Dates: 21 Jun ~ 16 Oct 2006


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


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

DSDM

Timeboxing format “MoSCoW” classification Example:


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


Documentation

Type of documents

Versioning Format

Revision Procedure


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)


Documentation

Versioning Format

Example:


Documentation

Change Management Control Procedure

Initial Version

Edit Document

Document Update?

Version X change


Delivery

Documentation Product (Software Application)

MPEG Video Installation CD Source Code User Manual*


Project Product

Bee Hive Simulator


Project Product

Product Overview Genetic Algorithm (GA)

Defintion Implementation Applications


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.


Product Overview

Main Features: Graphical Simulation Genetic Algorithms Technique (GA) Generate Report

Product Overview

Target Audience Researchers Students Bee Farmers


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.


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

GA Operators Include: Mutation Crossover

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

Genetic Algorithm

Project Demo

Bee Hive Simulator


Q & AFeel free to ask us…


End Of Presentation

THANK YOU!


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…

MoSCoW

MoSCoW stands for: M - MUST have this. S - SHOULD have this if at all possible. C - COULD have this if it does not affect

anything else. W - WON'T have this time but WOULD like

in the future.