Systems based on artificial intelligence, such as particle swarm optimization and geneticalgorithm have received increased attention in many research areas. One of the main objectives inthe gas metal arc welding (GMAW) process is to achieve maximum depth of penetration (DP) as acharacteristic of quality and stiffness. This article has examined the application of particle swarmoptimization algorithm to obtain a better DP in a GMAW and compare the results obtained with thetechnique of genetic algorithms. The effect of four main welding variables in GMAW process whichare the welding voltage, the welding speed, the wire feed speed and the nozzle-to-plate distanceon the DP have been studied. For the implementation of optimization, a source code has beendeveloped in MATLAB 8.3. The results showed that, in order to obtain the upper penetration depth,it is necessary that: the welding voltage, the welding speed and the nozzle-to-plate distance must beat their lowest levels; the wire feed speed at its highest level
