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This paper presents a particle swarm optimizer for production of endurance time excitation functions (ETEFs). These excitations are intensifying acceleration time histories that are used as input motions in endurance time (ET) method. The accuracy of the ET methods heavily depends on the accuracy of ET excitations. Unconstrained nonlinear optimization is employed to simulate these excitations. Particle swarm optimization (PSO) method as an evolutionary algorithm is examined in this paper to achieve a more accurate ETEF, where optimal parameters of the PSO are first determined using a parametric study on the involved variables. The proposed method is verified and compared with the trust‐region‐reflective method as a classical optimization method and imperialist competitive algorithm as a recently developed evolutionary method. Results show that the proposed method leads to more accurate ET excitations.

Development of an alternative PSO‐based algorithm for simulation of endurance time excitation functions