More of my philosophy about the poor local search ability of particle swarm optimization (PSO) in artificial intelligence..

[World-News2100]

Hello....


More of my philosophy about the poor local search ability of particle
swarm optimization (PSO) in artificial intelligence..

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

I will explain something important about particle swarm optimization
(PSO) in artificial intelligence:

In many research papers, it is proved that particle swarm optimization
(PSO) in artificial intelligence could provide faster convergence and
could find better solutions when compared to GA(genetic algorithm). The
implementation of PSO is also simple. But the main disadvantage of PSO
is with its poor local search ability. But you have to understand that
the poor local search ability of PSO is much more compensated by its
"faster" convergence, this is why i think that PSO is really useful
since you can even guarantee the optimal convergence in PSO as i am
learning you in my below thoughts and writing, so read them carefully.

More of my philosophy about evolutionary algorithms and artificial
intelligence..

You can read more about my education and my way of doing here:

Here is more proof of the fact that i have invented many scalable
algorithms and algorithms:

https://groups.google.com/g/comp.programming.threads/c/V9Go8fbF10k

And you can take a look at my photo that i have just put
here in my website(I am 53 years old):

https://sites.google.com/site/scalable68/jackson-network-problem

I think i am smart and I will explain more evolutionary algorithms such
as particle swarm optimization (PSO) and the genetic algorithm(and also
don't forget to read carefully my below new interesting proverb):

I think that Modern trends in solving tough optimization problems tend
to use evolutionary algorithms and nature-inspired metaheuristic
algorithms, especially those based on swarm intelligence (SI), two major
characteristics of modern metaheuristic methods are nature-inspired, and
a balance between randomness and regularity. And notice that i am
talking smartly below about the powerful modern evolutionary algorithm
that we call particle swarm optimization (PSO), and i think that the
powerful modern evolutionary algorithm that we call particle swarm
optimization (PSO) is also a balanced use of randomness with a proper
combination with certain deterministic components that is in fact the
essence of making such algorithms so powerful and effective, and notice
that the randommness in a genetic algorithm (GA) comes from the
randomness of mutations of chromosomes or in PSO it comes from the size
of the population that is constituted with the members that search also
randomly, and this randomness in artificial intelligence like PSO
and Reinforcement learning permits to move forward towards a better
global optimum of efficiency, and if the randomness in an algorithm is
too high, then the solutions generated by the algorithm do not converge
easily as they could continue to "jump around" in the search space. If
there is no randomness at all, then they can suffer the same
disadvantages as those of deterministic methods (such as the
gradient-based search). Therefore, a certain tradeoff is needed.

More of my my philosophy about the Exploration/Exploitation trade off in
AI(artificial intelligence)..

In Reinforcement Learning in AI(artificial intelligence), for each
action (i.e. lever) on the machine, there is an expected reward. If this
expected reward is known to the Agent, then the problem degenerates into
a trivial one, which merely involves picking the action with the highest
expected reward. But since the expected rewards for the levers are not
known, we have to collate estimates to get an idea of the desirability
of each action. For this, the Agent will have to explore to get the
average of the rewards for each action. After, it can then exploit its
knowledge and choose an action with the highest expected rewards (this
is also called selecting a greedy action). As we can see, the Agent has
to balance exploring and exploiting actions to maximize the overall
long-term reward. So as you are noticing i am posting below my
just new proverb that talks about the Exploration/Exploitation trade off
in AI(artificial intelligence), and you also have to know how to build
correctly "trust" between you and the others so that to optimize
correctly, and this is why you are seeing me posting my thoughts like i
am posting.

You have to know about the Exploration/Exploitation trade off in
Reinforcement Learning and PSO(Particle Swarm Optimization) in AI by
knowing the following and by reading my below thoughts about artificial
intelligence:

Exploration is finding more information about the environment.

Exploitation is exploiting known information to maximize the reward.

This is why i have just invented fast the following proverb that also
talks about this Exploration/Exploitation trade off in AI (artificial
intelligence):

And here is my just new proverb:

"Human vitality comes from intellectual openness and intellectual
openness also comes from divergent thinking and you have to well balance
divergent thinking with convergent thinking so that to converge towards
the global optimum of efficiency and not get stuck on a local optimum of
efficiency, and this kind of well balancing makes the good creativity."

And i will explain more my proverb so that you understand it:

I think that divergent thinking is thought process or method used to
generate creative ideas by exploring many possible solutions, but notice
that we even need openness in a form of economic actors that share ideas
across nations and industries (and this needs globalization) that make
us much more creative and that's good for economy, since you can easily
notice that globalization also brings a kind of optimality to divergent
thinking, and also you have to know how to balance divergent thinking
with convergent thinking, since if divergent thinking is much greater
than convergent thinking it can become costly in terms of time, and if
the convergent thinking is much greater than divergent thinking you can
get stuck on local optimum of efficiency and not converge to a global
optimum of efficiency, and it is related to my following thoughts about
the philosopher and economist Adam Smith, so i invite you to read them:

https://groups.google.com/g/alt.culture.morocco/c/ftf3lx5Rzxo

More philosophy about what is artificial intelligence and more..

I am a white arab, and i think i am smart since i have also invented
many scalable algorithms and algorithms, and when you are smart you will
easily understand artificial intelligence, this is why i am finding
artificial intelligence easy to learn, i think to be able to understand
artificial intelligence you have to understand reasoning with energy
minimization, like with PSO(Particle Swarm Optimization), but
you have to be smart since the Population based algorithm has to
guarantee the optimal convergence, and this is why i am learning
you how to do it(read below), i think that GA(genetic algorithm) is
good for teaching it, but GA(genetic algorithm) doesn't guarantee the
optimal convergence, and after learning how to do reasoning with energy
minimization in artificial intelligence, you have to understand what is
transfer learning in artificial intelligence with PathNet or such, this
transfer learning permits to train faster and require less labeled data,
also PathNET is much more powerful since also it is higher level
abstraction in artificial intelligence..

Read about it here:

https://mattturck.com/frontierai/

And read about PathNet here:

https://medium.com/@thoszymkowiak/deepmind-just-published-a-mind-blowing-paper-pathnet-f72b1ed38d46

More about artificial intelligence..

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..

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.

And read my following thoughts of my philosophy about what is smartness:

https://groups.google.com/g/alt.culture.morocco/c/Wzf6AOl41xs

And read my following thoughts of my philosophy about my new proverbs
and about dignity:

https://groups.google.com/g/alt.culture.morocco/c/72FdpcFe9Vk


Thank you,
Amine Moulay Ramdane.

[World-News2100]

Hello...

More of my philosophy about the poor local search ability of particle
swarm optimization (PSO) in artificial intelligence..

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

I am posting again my following thoughts since i think they are interesting: