Literature Review of Particle Swarm Optimization

Edi Kurniawan; Diana Alia; Henna Nurdiansari; Sofyan Putra

doi:10.36101/msm.v17i2.382

Edi Kurniawan Maritime Polytechnic of Surabaya, Surabaya, Indonesia
Diana Alia Maritime Polytechnic of Surabaya, Surabaya, Indonesia
Henna Nurdiansari Maritime Polytechnic of Surabaya, Surabaya, Indonesia
Sofyan Putra Maritime Polytechnic of Surabaya, Surabaya, Indonesia

DOI: https://doi.org/10.36101/msm.v17i2.382

Keywords: OPTIMIZATION, pBest, gBest

Abstract

_{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.}

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