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Endurance Time Acceleration Functions are specially predesigned intensifying excitation functions that their amplitude increases with time. On the other hand, wavelet transform is a mathematical tool that indicates time variations of frequency in a signal. In this paper, an approach is presented for generating endurance time acceleration functions (ETAFs) whose response spectrum is compatible with the European Code regulations (EC8) elastic spectrum. Method applied is a modification of data in time and frequency domain. For this purpose, wavelet transform has been used to decompose a series of random points to several levels such that each level covers a special range of frequency, then every level is divided into the numbers of equal time intervals and each interval of time is multiplied by a variable. Subsequently, the mathematical unconstrained optimization algorithm is used to calculate the variables and minimize error between response and target spectra. The prosed procedure is used in two methods. Then with two methods, two different acceleration functions are produced.

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Meta-heuristics have received increasing attention in recent years. The present article introduces a novel method in such a class that distinguishes a number of artificial search agents called players within two teams. At each iteration, the active player concerns some other players in both teams to construct its special movements and to get more score. At the end of some iterations (like quarters of a sports game) the teams switch their places for fair play. The algorithm is developed to solve a general purpose optimization problem; however, in this article its application is illustrated on structural sizing design. Switching Teams Algorithm is presented as a parameter-less population-based algorithm utilizing just two control parameters. The proposed method can recover diversity in a novel manner compared to other meta-heuristics in order to capture global optima.

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This article presents a new meta-heuristic optimization algorithm based on the power of human thinking and decision-making, which will be called Gold Rush Optimization (GRO). The thinking and decision-making ability of humans were used in this paper to develop a approach to create an optimization method. The hypothetical interaction between human operators in search of gold, based on the sound volume received from metal detectors, was used to develop the method. Benchmark functions, engineering design examples, and truss structures (which were optimized using different algorithms previously) were used for validation and verification of the proposed algorithm. MATLAB was used for programming. The CEC 2005 benchmark functions obtained reached the global target minimum, and the numerical engineering and truss examples were improved compared to the previous algorithms. Therefore, the proposed algorithm can be used as an alternative for the previously developed meta-heuristic optimization algorithms, which can be used in all optimization fields.