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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.

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The damage identification of truss constructions was investigated in this work. Damage detection is defined through an inverse optimization problem. A function defined as a combination of mode shapes and natural frequencies is examined to minimize damage structures. This guided approach considerably reduces the computational cost and increases the accuracy of optimization. This index mostly exhibits an acceptable performance. Gold Rush Optimization (GRO), an artificial intelligence system based on the power of human thinking and decision-making, was employed to address damage detection. The programming was done in MATLAB. Validation and verification were carried out using a 10, 25, 200, 272, and 582 bar truss. A comparison between the GRO, MCSS, PSO and TLBO is conducted to show the efficiency of the GRO in finding the global optimum. The results show that utilizing the proposed function and the GRO optimization technique to discover truss damaged structure in the quickest time possible is both reliable and stable.