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Nowadays, meta-heuristic algorithms have earned special position in optimization problems, particularly\udnonlinear programming. In this study, a new meta-heuristic algorithm called "Improvement of position\ud(IMPRO Algorithm)" is recommended to solve the optimization problems. This algorithm, similar to other\udheuristic and meta-heuristic algorithms starts with production of random numbers. However, the\udaforementioned algorithm is inspired by humans’ behavior to enhance the position which coincidentally\uddetects the best position with respect to various conditions. Subsequently, a position with the least standard\uddeviation (0.01) is created surrounding random numbers around the situation in the form of normal\uddistribution. Afterwards, the new top position is considered and the two positions are compared and the top\udposition is determined. Thus the conditions which created the best position are situated as the motion\udfactors. Naturally, the motion direction is toward the opposite direction of the lower position. Next, this\udalgorithm changes a response during the search process and solves the problem by utilizing the firm\uddecision

Providing new meta-heuristic algorithm for optimization problems inspired by humans' behavior to improve their positions