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I would like to know what is a good, stable, framework that you guys suggest for implementing evolutionary optimisation techniques like PSO or GA?
I have written my own ones and I like them, I would be interested in comparing or adding to existing stable ones (or just using them if they are solid and extensible).
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The results of GA and PSO implementation in the bus timetabling problem show that the PSO algorithm is superior in finding the optimal solution in terms of accuracy and iteration. In addition the PSO algorithm is also superior to the simplicity of the techniques used.
The genetic algorithm (GA) is the most popular of the so-called evolutionary methods in the electromagnetics community. Recently, a new stochastic algorithm called particle swarm optimization (PSO) has been shown to be a valuable addition to the electromagnetic design engineer's toolbox.
The first value is the best personal solution the particle has found so far. The second one is the best global solution that the swarm of particles has found so far. So each particle has in memory its best personal solution and the best global solution.
As per my observation, PSO has the following advantages over GA: Simple concept, easily programmable, faster in convergence and mostly provides better solution. PSO and GA are based on the same principle. A random element and the cost of error. They are useful for different applications.
Well, it depends on how sophisticated and complicated problems you need to solve, but in most cases the problem is such simple, that for example PSO algorithm is quite simple.
Maybe just try to find some good framework for matrix operations and implement it on your own?
But as I said, it all depends on the complexity of your problem. I used the PSO for quite simple problems (finding minimas of very complicated functions etc...) so I never needed any framework.
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