Comparative Analysis of the Modification of the Inertia Parameter for the Improvement of the PSO Algorithm Performance
DOI:
https://doi.org/10.46842/ipn.cien.v25n1a09Keywords:
PSO, inertia weight, optimization, chaotic inertiaAbstract
In this work an improvement of the metaheuristic algorithm called Particle Swarm Optimization (PSO) is shown. The PSO algorithm is inspired in the behavior that groups of individuals from nature exhibit, it can be mentioned for example flocks and shoals. Each individual or particle, on a mathematical process in an analogue manner, is considered as a possible solution and from them it is contemplated, as relevant information, its position, and velocity. The velocity of each particle is modified as it is multiplied by a parameter named inertia weight and it is this parameter that we propose to modify for the improvement of the performance of the algorithm. The variation of the inertia weight develops as the following two manners, linear decreasing, and chaotic decreasing. The functions Eggholder and Six-Hump Calmelback were considered to determine the improvement of the performance in the PSO algorithm. The reported results in this work indicate a better performance in the application of chaotic decreasing to the inertia weight.
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