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For most people, flash player version seems unimportant. Well, what is difference between flash player 9 or 10, as long as application wanted running well. But for flash developer, it needs more attention. Version issues had been long known for programmers in every programming languange.
fyuu… I’m exhausted.. ^ ^
There are so many work to do here right now..
If I recall, when I was still in college, I could do my office work at home, and almost all of my free time were spent for playing games. While I was playing, I will let the TV turned on so my room’s not too quiet. When I have exams and I need to study, sometimes I played games while reading my notes. That made me feel relaxed.
When I was making 3D models for Agate and got fed up because it turns out not as I expected, usually I’d play a game to see the 3D models. Looking at good 3D character models would made me “burning” with passion to continue on working my 3D models… although in the end, the outcome wasn’t as good as the game’s model…. XD. I’m still learning.
Every Saturday night, I was very excited because I can forget every college tasks at weekends. It would be a waste to spend the weekend by sleeping all day, so I’d play all night long, watch a movie, or practicing to improve my skills, and ended up lack of sleeping on Mondays.
One day I want to be able to do those routines, but I think, I’d build Agate first.
There are other things far more important that I want to achieve here
^ ^ my dream…
Particle swarm optimization is developed by Elbert and Kennedy in 1995. This algorithm mimics the birds flocking. When the birds are searching for food, they never collide each other and they have information of best solution to food place of that group. Finally, they all can reach the food (solution).
In general, This algorithm work as follow:
- Each bird is an agent
- Each Agent flies randomly through the problem space(Search space of food) and the problem solution is generated.
- The solution is checked by the fitness function. Each agent has the best solution of his group (global best) and the local best (best solution of himself) of fitness value, x and y coordinates.
- If one agent sees the solution, the others will follow quickly
- If not, then the agent will fly according to the local best and the global best.
- Go to number 2.
From these, we have 3 key point of this algorithm: Velocity, Position, Fitness Value. Velocity and position is updated every loop. Fitness value determine how good the solution found. If the solution is better than before, agent will store the solution (either it is local or global).
PSO can be implemented in collision avoidance, path searching, clustering, scheduling, and so on.
I used to play games a lot. I bring my DS to classes, play on my PC/Consoles, and so on. I’m pretty sure there’s a good 30-40 hours per week spent for gaming. That was pretty tame compared to my friends, but that’s still a lot of time
Then I thought, “Well this is fun. I wonder how these things are made. I want to be a game developer!”
I did. Then my gaming hours dropped hard.