When should I use Genetic Algorithms as opposed to Particle Swarm Optimization?

Question

For which problems are Genetic Algorithms more suitable than Particle Swarm Optimization, and vice-versa? Are there any guidelines?

Answer 1

These kind of questions cannot be answered without looking at a particular project. Each algorithm has its particular strengths and weaknesses; and trade-offs in terms of use of resources (processing power and/or storage space, for example). If there was an objective answer, then the worse algorithm would surely fall in disuse.

It also depends what you mean by "better". Faster? Better score according to some evaluation measure? More robust (ie works with many diverse data sets)?

I would recommend looking at both algorithms in more detail, and trying to understand how they work. Then you should be able to find out which best fits your problem.

However, one problem with Particle Swarm Optimisation is that it is not well understood, so you might have to resort to trial-and-error.

Answer 2

The paper Comparison between genetic algorithms and particle swarm optimization (1998, by Eberhart and Shi) does not really answer the question of when to use one over the other (this may be an open question), but at least it provides a comparison of how the methods work and what could affect their performance (i.e. which parameters or operators they use, and what the typical values are), so it may be worth reading it.