More about artificial intelligence..

[amin...@gmail.com]

Hello..


More about artificial intelligence..

You have to understand me, i am going really fast in understanding artificial intelligence, since i think i am a white arab that is smart since i have invented many scalable algorithms, but i think one of the most important part in artificial intelligence is reasoning with energy minimization, it is the one that i am working on right now, see the following video to understand more about it:

Yann LeCun: Can Neural Networks Reason?

https://www.youtube.com/watch?v=YAfwNEY826I&t=250s

I think that since i have just understood much more artificial intelligence, i will soon show you my next Open source software project that implement a powerful Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO, and i will write an article that explain
much more artificial intelligence and what is smartness and what is
consciousness and self-awareness..

Read my previous thoughts to understand:

I am a white arab, and i think i am smart since i have invented many scalable algorithms..

And in only one day i have just learned "much" more artificial intelligence, i have read the following article about Particle Swarm Optimization and i have understood it:

Artificial Intelligence - Particle Swarm Optimization

https://docs.microsoft.com/en-us/archive/msdn-magazine/2011/august/artificial-intelligence-particle-swarm-optimization

But i have just noticed that the above implementation doesn't guarantee the optimal convergence.

So here is how to guarantee the optimal convergence in PSO:

Clerc and Kennedy in (Trelea 2003) propose a constriction coefficient parameter selection guidelines in order to guarantee the optimal convergence, here is how to do it with PSO:

v (t+1) = k*[(v(t) + (c1 * r1 * (p(t) – x(t)) + (c2 * r2 * (g(t) – x(t))]

x(t+1) = x(t) + v(t+1)

constriction coefficient parameter is:

k = 2/abs(2-phi-sqrt(phi^2-(4*phi)))

k:=2/abs((2-4.1)-(0.640)) = 0.729

phi = c1 + c2

To guarantee the optimal convergence use:

c1 = c2 = 2.05

phi = 4.1 => k equal to 0.729

w=0.7298

Population size = 60;


Also i have noticed that GA(genetic algorithm) doesn't guarantee the optimal convergence, and SA(Simulated annealing) and Hill Climbing are much less powerful since they perform only exploitation.

In general, any metaheuristic should perform two main searching capabilities (Exploration and Exploitation). Population based algorithms ( or many solutions ) such as GA, PSO, ACO, or ABC, performs both Exploration and Exploitation, while Single-Based Algorithm such as SA(Simulated annealing), Hill Climbing, performs the exploitation only.

In this case, more exploitation and less exploration increases the chances for trapping in local optima. Because the algorithm does not have the ability to search in another position far from the current best solution ( which is Exploration).

Simulated annealing starts in one valley and typically ends in the lowest point of the same valley. Whereas swarms start in many different places of the mountain range and are searching for the lowest point in many valleys simultaneously.

And in my next Open source software project i will implement a powerful
Parallel Linear programming solver and a powerful Parallel Mixed-integer programming solver with Artificial intelligence using PSO.


Thank you,
Amine Moulay Ramdane.