Hi PSO algorithms such as GA are heuristic methods. They can be useful when the gradient of the objective function is not available but if your problem is of medium size and if your gradients are available it is in general better to use more classical methods.

Their performance can be positively and negatively affected by their parameter settings. It is worth implementing them and use some non-parametric and group statistical tests to establish their performance.

Hi, I recently published a paper entitled " Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach", so-called SRSR optimization algorithm. I also shared MATLAB code of this algorithm as well as 19 other well-known heuristic evolutionary algorithms in form of one free package for the sake of a fair comparison on the Internet. Based on my experiences, each algorithm can be suitable for a specific set of problems based on its inherent features and formulation; in terms of convergence quality and convergence speed. If an algorithm has outstanding performance on some problems does not necessarily guaranty that can be outstanding for other problems as well. Many people believe that some algorithms like PSO are completely dated and you should not apply them to any problem anymore; However I found that traditional PSO has a better performance than several new algorithms in some special case-studies. Finally, there is no other option in my idea, you should test a bunch of algorithms to find best one suited to your problem. Moreover, some of algorithms have a good performance in small-scale problems and others are better in case of large scale. In my vision, quality of an algorithm is 100% percent related to the problem you wanna to apply on. Some time, some algorithms suffer from low pace because their computational cost is high, however they can find very high quality solutions. On the other side, they may have high convergence speed with lower convergence quality.

From another point of view, publication of more than tens evolutionary algorithms during a year, as you said, may assist other researchers to find some novel more applicable ideas in case of applied soft computing; because this is a step-by-step evolutionary procedure. Some time the best option is to simply combine to algorithms and enjoy abilities of both ones to solve your problem; however it is not a new optimization algorithm.