I have recently become very interested in search optimization algorithms. One that I am going to apply is to use particle swarm optimization. However I am confused by the descriptions of the algorithm I have read as they seem to 'not mention' a key step. That is after applying the discrete steps then I would expect an instruction that says 'and now make a change to the particles in the swarm and re-evaluate the status of the particles, repeating the evaluating and scoring etc'.

For example if this were the travelling salesman:

Quote:

//(Generate a swarm of random solutions to the map i.e. the particles)

For each particle

Initialize particle

END

//Then.. (pBest is the top solution found in the history of that particle, gBest is the global best found amongst all particles)

Do

For each particle

Calculate fitness value

If the fitness value is better than the best fitness value (pBest) in history

set current value as the new pBest

End

Choose the particle with the best fitness value of all the particles as the gBest

For each particle

Calculate particle velocity according equation (a)

Update particle position according equation (b)

End

While maximum iterations or minimum error criteria is not attained

Is this just implicit anyway? I understand that not all problems are suited to all methods but i think that a salesman map entity fits it just fine, so long as there is an evolution step, otherwise the scores would just mill around the population without anything actually changing in terms of an overall optimum solution.

I must be missing something?

[EDIT]

I see that searching for PSO with travelling salesman yields some interest so I will have a read also. My problem is combinatric in nature also, not TSP but shares similar attributes. ]]>