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This paper proposes a new numerical procedure, based on an evolutionary optimization algorithm, for the simultaneous generation of the three components of the seismic ground acceleration at sites in the vicinity of recording stations. Different from other studies proposed to simulate only one component of the seismic excitation, in this paper, the three components are generated simultaneously in a single stage. The methodology allows the determination of a train of seismic waves, modeled as Morlet's wavelets, that are next used for the generation of ground acceleration components. The parameters of each wave, i.e., amplitude, frequency, duration, time of arrival, and direction, are determined using an evolutionary optimization algorithm. The seismic wave train is validated by comparison with the recorded accelerograms and respective response spectra. Next, these same seismic waves are used for the simultaneous simulation of the three seismic acceleration time histories at locations on the surface close to the seismic station. The results suggest that the proposed methodology can be used for engineering applications in which the ground motion must be specified at various points, for instance in the case of electric power transmission lines or at bridge supports. Highlights: A complete methodology to simultaneously generate the 3 components of seismic accelerograms is proposed in this work. A train of seismic waves is obtained, via Morlet's wavelets, which allows simulating accelerograms at different points. The results showed that the proposed method is a simple and effective tool that can be used in practical situations. [ABSTRACT FROM AUTHOR]

Dynamic economic dispatch with valve-point effect (DED_vpe) is a dynamic nonlinear high-dimensional optimization problem with non-smooth and non-convex characteristics. Meta-heuristic methods have become the mainstream for solving the DED_vpe problem. However, most of these methods only focus on minimizing the generation costs and ignore the algorithmic robustness. In this paper, an adaptive backtracking search optimization algorithm with a dual-learning strategy (DABSA) is proposed for solving the DED_vpe problem. In DABSA, a dual-learning strategy (DL) based on the current and historical optimal individuals is developed to update each individual. This updating strategy helps DABSA improve solution accuracy and overcome premature convergence. In addition, an adaptive parameter control mechanism (APC), which can automatically adjust parameter 'mixrate' value according to the current iteration number, is presented. To handle the system constraints, a 'repair + penalty' constraints-handling approach is employed to lead non-feasible solutions towards the feasible region quickly. The performance of DABSA is assessed by testing on four DED problems containing 5, 10 and 30 units. The experimental results show that DABSA is very competitive compared with reported representative methods in yielding low fuel costs along with high robustness. • A new method DABSA is proposed for solving DED problem with valve-point effects. • The core innovation of DABSA lies in the design of a dual learning strategy (DL). • BSA is the first attempt in solving DED problem with valve-point effects. • Results on 5, 10 and 30 units show that DABSA is competitive in terms of solution accuracy and robustness. [ABSTRACT FROM AUTHOR]

Dynamic economic dispatch with valve-point effects (DED_ vpe) is a high-dimensional constrained optimization problem with non-convex and non-smooth characteristics. Hybrid methods are one of the most advanced methods to solve the problem. However, most of these methods improve the solution accuracy at the expense of algorithm robustness. This paper proposes an adaptive hybrid backtracking search optimization algorithm (AHBSA) for solving the DED_ vpe. The core idea of AHBSA lies in designing a suitable coupling structure based on the current best individual (called optimal partial coupling). The structure hybridizes an improved BSA mutation operator and the DE/best/1 operator with equal probability. The improved BSA mutation operator uses the current best individual and the historical population to update individual position, called BSA/best/old. It is also the first research work of extending BSA to the problem. In addition, an adaptive parameter control mechanism is proposed to select an appropriate 'mixrate' value for achieving better coupling. The performance of AHBSA is validated on six DED test cases of three systems. Experimental results demonstrate that, compared with some representative methods, AHBSA not only reduces the fuel cost but also ensures the robustness of the algorithm. • A hybrid method AHBSA is proposed to solve DED problem with valve-point effects. • An optimal partial coupling structure is designed in AHBSA. • BSA is the first attempt at solving DED problem. • Results on six benchmark DED test cases show AHBSA is competitive. [ABSTRACT FROM AUTHOR]