Esperanza García Gonzalo

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—Particle swarm optimization (PSO) can be interpreted physically as a particular discretization of a stochastic damped mass-spring system. Knowledge of this analogy has been crucial to derive the PSO continuous model and to introduce different PSO family members including the generalized PSO (GPSO) algorithm, which is the generalization of PSO for any time(More)
Inverse problems are ill-posed: the error function has its minimum in a flat elongated valley or surrounded by many local minima. Local optimization methods give unpredictable results if no prior information is available. Traditionally this has generated mistrust for the use of inverse methods. Stochastic approaches to inverse problems consists in shift(More)
PArtIclE swArM OPtIMIzAtION (PsO) APPlIED tO INvErsE PrOBlEMs Particle swarm optimization is a stochastic evolutionary computation technique inspired by the social behavior of individuals (called particles) in nature, such as bird flocking and fish schooling (Kennedy & Eberhart, 1995). Let us consider an inverse problem of the form F m d () = , where m M ∈(More)