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In this paper, after a literature review, studies will be concentrated on standard deviation of invasive weedoptimization's normal distribution function which is used for distributing seeds of each weed over the search space. Although invasive weed optimization is a great algorithm to solve real world practical optimization problems but there is a serious drawback in distributing the seeds over the search space. A new concept will be presented to distribute seeds of each weed over the search space which increases the robustness and effectiveness of algorithm, and therefore leads to an improved invasive weed optimization. Simulation on a set of unconstrained benchmark functions reveals the superiority of the proposed algorithm in quick convergence and finding better solutions compared to the original invasive weed optimization.