| just downvoting every one of my comments because you disagree with them? That's cool. 1) Just one example? Here: http://adslabs.org/adsabs/abs/2009PhDT.........4C/ 2) You are trying to find the global maximum. How do you not understand the value of communication when searching for a maxima in the likelihood distribution? You're just being intentionally obtuse. 3) Yes, really. A story: You have a landscape with mountains and hills and you have one person trying to find the tallest mountain. That person is blind, they can't see shit. That person is also mute and deaf. Their only sense is a vibrating altimeter. They get drunk, and climb mountains for 100000 days, trying to find the tallest mountain. You are advocating the idea that you should send 1000 of these blind deaf mutes out there one at a time (running these in parallel is just faster serial) and then they should vote on which mountain is the tallest at the end. I'm saying you should send a bunch of not-deaf-mutes out there (ie implement mutation, breeding, cross-contamination, gravitation, whatever) so they can tell each other where the stupid mountains are (this requires _parallel_) and they don't waste their whole time stumbling around (burning in). You're just being intentionally argumentative. |
1) This is an abstract of a PhD thesis which makes no mention of particle swarms, I couldn't find the full text. This is your evidence?
2) No this isn't about looking for the global maximum. Several people have explained to you that this is a sampling algorithm, but you still fail to show understanding of the difference.
3) Your story doesn't demonstrate your point and you do not understand the argument I presented to you. The types of algorithms suited for an army of people with calculators 100 years ago isn't fundamentally different from the type of algorithm suited for computers today, and if anything, it's likely to be more sequential, not less.