It can be simple and fast for solving some contrained optimization problems but it can easily stuck in a local minimum. It is greatly affcted by the starting imposed values of the variables where changing the starting points might change the local minimum obtained especially if your problem contain lots of local minima. I advice you to see global optimization problems as the GAs and the PSOs.

https://scicomp.stackexchange.com/questions/772/what-are-the-advantages-disadvantages-of-interior-point-methods-over-simplex-met

http://www.dep.ufscar.br/docentes/munari/slidesBWIPM2015.pdf

There is no "the" interior point method - there are hundreds of variations. As has been mentioned before in general an interior point method is not a global optimiser - as it relies on local optimality criteria that are sufficient for global optimality only for convex programs with interior points. And - as is usually the case these days - I signal that one should be wary of the use of so-called global optimisers of the "genetic" variety - especially if you call yourself a scientist, and you actually work in a mathematical field.

Whether it makes sense to use it in an application in optimal transmission depends largely on these things - if the properties of the problem at hand are enough to ensure convergence to a global optimizer then use it, otherwise don't.

Thanks a lot for your kind feedback, 

Thank you Michael for correcting my information about interior point