Abstract
As presented in Chap. 1, Darwinian particle swarm optimization (DPSO) presented by Tillett et al. (Darwinian particle swarm optimization, 2005) 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. Despite its superior performance when compared to its nonevolutionary counterpart, the DPSO also exhibits a key drawback: its computational complexity. This chapter proposes a method for controlling the convergence rate of the DPSO using fractional calculus (FC) concepts (Pires et al., Journal on Nonlinear Dynamics, 61(1–2), 295–301, 2010). The fractional-order optimization algorithm, denoted fractional-order Darwinian particle swarm optimization (FODPSO), is then tested using several well-known functions and the relationship between the fractional-order velocity and the convergence of the algorithm is observed.