Literature Review of Particle Swarm Optimization - Edi Kurniawan; Diana Alia; Henna Nurdiansari; Putra, Sofyan (Meteor STIP Marunda, 2024)
Optimization methods are crucial methods in a process because optimization methods can solve complex problems. One of the most effective optimization methods to achieve optimal solutions is Particle Swarm Optimization (PSO), an algorithm inspired by the social behavior of animals. Where, the PSO algorithm is a particle (parable an animal) that has been initialized will move continuously updating its position based on a combination of two factors, namely the attraction towards the individual's best position (pBest) and the attraction towards the global best position (gBest) until it reaches the position optimal. Particle movement is influenced by three main control parameters, namely cognitive coefficient (c1), social coefficient (c2), and inertial weight (ω) in order to produce optimal values ​​without being trapped in local solutions. The advantages of PSO compared to other optimal methods such as the Firefly Algorithm (FA) and Gray Wolf Optimizer (GWO) are its convergence speed and ability to handle non-linear problems with noise. This makes PSO good for applying to complex problems such as solving non-linear mathematical model problems, optimizing fuzzy controllers, optimizing exhaust gas emission parameters and engine performance on ships.