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Advisor(s)
Abstract(s)
One of the most well-known bio-inspired algorithms
used in optimization problems is the particle swarm
optimization (PSO), which basically consists on a machinelearning
technique loosely inspired by birds flocking in
search of food. More specifically, it consists of a number
of particles that collectively move on the search space in
search of the global optimum. The Darwinian particle swarm
optimization (DPSO) is an evolutionary algorithm that extends
the PSO using natural selection, or survival of the fittest,
to enhance the ability to escape from local optima. This
paper firstly presents a survey on PSO algorithms mainly
focusing on the DPSO. Afterward, a method for controlling
the convergence rate of the DPSO using fractional calculus
(FC) concepts is proposed. The fractional-order optimization
algorithm, denoted as FO-DPSO, is tested using several
well-known functions, and the relationship between the fractional-order velocity and the convergence of the algorithm
is observed. Moreover, experimental results show that
the FO-DPSO significantly outperforms the previously presented
FO-PSO.
Description
Keywords
Fractional calculus DPSO Evolutionary algorithm