Abstract
AbstractOne of the main issues in the application of a particle swarm optimization (PSO) algorithm and of every evolutionary optimization algorithm is the finding of the suitable parameters of the algorithm. Usually, a trial and error procedure is used but, also, a number of different procedures have been applied in the past. In this chapter, we use a new adaptive version of a PSO algorithm where random values are assigned in the initialization of the algorithm and, then, during the iterations the parameters are optimized together and simultaneously with the optimization of the objective function of the problem. This idea is used for the solution of the probabilistic traveling salesman problem (PTSP). The algorithm is tested on a number of benchmark instances and it is compared with a number of algorithms from the literature.KeywordsParticle swarm optimizationVariable neighborhood searchProbabilistic traveling salesman problem
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