Acceleration Particle Swarm Optimization

EasyChair Preprint 15839

Abstract

Particle Swarm Optimization (PSO) is a popular optimization algorithm for solving complex optimization problems. Many PSO algorithms were proposed in literature where Velocity was calculated first and then it was added to position to obtain new position. In this work, a novel algorithm titled “Acceleration Particle Swarm Optimization (AccPSO)” is proposed where acceleration is calculated first and then displacement is obtained next with initial velocity, acceleration and time. Displacement is added to position to get new position. Unlike many PSO algorithms in literature, where iterations and time are used interchangeably, the time “t” in AccPSO algorithm is a continuous variable. In this work, AccPSO, PSO, Acceleration-based Particle Swarm Optimization (APSO) and APSOc (APSO with clamping) are tested on seven benchmark functions. Results obtained are discussed. It has been found that AccPSO with time “t” = 0.1 and “t” = 0.25 between iterations yielded optimal results when tested on benchmark functions.

Keyphrases: AccPSO, Acceleration Particle Swarm Optimization, PSO, Particle Swarm Optimization, acceleration

@booklet{EasyChair:15839,
  author    = {Satish Gajawada},
  title     = {Acceleration Particle Swarm Optimization},
  howpublished = {EasyChair Preprint 15839},
  year      = {EasyChair, 2025}}