On the Analysis of Performance of the Artificial Tribe Algorithm

Abstract

Artificial Tribe Algorithm (ATA) is a novel intelligent optimization algorithm based on the simulation of bionic intelligent optimization algorithm. This work discusses the main factors which influence the performance of ATA, and compares the performance of ATA with that of genetic algorithm (GA), particle swarm optimization (PSO), and artificial fish-swarm algorithm (AFSA) for optimization multivariable functions. The simulation results showed that ATA outperforms the mentioned algorithms in global optimization problems.