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

2. 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

3. Project Team KHO PUAY MENG 3059571 KOH MENG HONG 3059686 NG CHIOU YOONG 3064827 YIP CHEW HONG 3059546 University of Wollongong, Australia

4. 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

5. 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

6. Proposal Study Subject: Colony Creature: Honey Bee Literature Review & Research on Honeybee University of Wollongong, Australia

7. Proposal Solution: Model Honeybee population growth in dynamic data Provide Graphical Simulation on Bee behaviors such as: Building Scouting Foraging Patrolling Swarming Attack behaviors

8. Project Management Bee Hive Simulator University of Wollongong, Australia

9. Project Management Schedule Methodology Tools Documentation Delivery University of Wollongong, Australia

10. Schedule Total duration: <5 months Dates: 21 Jun ~ 16 Oct 2006 University of Wollongong, Australia

11. 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

12. 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

13. DSDM Timeboxing format “MoSCoW” classification Example: University of Wollongong, Australia

14. 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

15. Documentation Type of documents Versioning Format Revision Procedure University of Wollongong, Australia

16. 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

17. Documentation Versioning Format Example: University of Wollongong, Australia

18. Documentation Change Management Control Procedure Initial Version Edit Document Document Update? Version X change University of Wollongong, Australia

19. Delivery Documentation Product (Software Application) MPEG Video Installation CD Source Code User Manual* University of Wollongong, Australia

20. Project Product Bee Hive Simulator University of Wollongong, Australia

21. Project Product Product Overview Genetic Algorithm (GA) Defintion Implementation Applications University of Wollongong, Australia

22. 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

23. Product Overview Main Features: Graphical Simulation Genetic Algorithms Technique (GA) Generate Report

24. Product Overview Target Audience Researchers Students Bee Farmers University of Wollongong, Australia

25. 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

26. 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 : 3Water Index : 1 Chromosome A

27. 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

28. Genetic Algorithm GA Operators Include: Mutation Crossover

29. Genetic Algorithm Mutation Food Index : 1Water Index : 1 Food Index : 1Water Index : 3 Chromosome A Before Mutation After Mutation

30. Genetic Algorithm Crossover Food Index : 1Water Index : 3 Parent A Before Crossover Food Index : 2Water Index : 4 Parent B After Crossover Food Index : 1Water Index : 4 Offspring A Food Index : 2Water Index : 3 Offspring B

31. Genetic Algorithm

32. Project Demo Bee Hive Simulator University of Wollongong, Australia

33. Q & A Feel free to ask us… University of Wollongong, Australia

34. End Of Presentation THANK YOU! University of Wollongong, Australia

35. 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

36. 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…

37. 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.