Robust Particle Swarm Optimizer based on Chemomimicry

Autor:	Kneale, Casey, Booksh, Karl S.
Rok vydání:	2017
Předmět:	Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	A particle swarm optimizer (PSO) loosely based on the phenomena of crystallization and a chaos factor which follows the complimentary error function is described. The method features three phases: diffusion, directed motion, and nucleation. During the diffusion phase random walk is the only contributor to particle motion. As the algorithm progresses the contribution from chaos decreases and movement toward global best locations is pursued until convergence has occurred. The algorithm was found to be more robust to local minima in multimodal test functions than a standard PSO algorithm and is designed for problems which feature experimental precision. Comment: Revision # 2. Included pseudo code, more references, changed description
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1702.00993 Zobrazit plný text záznamu View this record from Arxiv