I dive into Particle Swarm Optimization (PSO) using MATLAB. With 1000 particles, I tackled the Himmelblau Function, going full detective to understand PSO from its original research. It was a game-changer in algorithm exploration

Hey, fellow code enthusiasts! You know that feeling when you stumble upon a super cool algorithm and you’re like, “I’ve GOT to try this out”? Well, that was me with the Particle Swarm Optimization (PSO) algorithm.

Results Results

The mission? Using MATLAB magic to unleash 1000 particles (yes, a grand thousand!) to dive deep into the valleys and peaks of the Himmelblau Function. I mean, who wouldn’t want to hunt for that elusive lowest point on such a mesmerizing mathematical landscape?

And get this, my curiosity about PSO didn’t stop at just admiration from afar. Nope! I went full detective mode, scouring the web to find its OG research paper. There’s just something so authentic about understanding an algorithm right from its roots, ya know?

After days of brewing up codes on MATLAB and embracing the true power of PSO, I gotta say – this algorithm? Absolutely game-changing.

Until next time, keep coding and keep exploring! ✌️



PSO 1000 iteration Modified Himmelblau Function (MH) without w

PSO 1000 iteration Modified Himmelblau Function (MH)

Two Dimension PSO