This study introduces a new search method for box-constrained optimization problems entitled Search Method for Box Optimization (SMBO). SMBO is a population heuristic based search methodology which solves global optimization problems. SMBO represents the population as Probability Density Function (PDF) inside the problem bounds. The PDF shape is dynamically adapted during the process to guide to a "good" search domain. The applicability and the efficiency of the method are demonstrated using

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... benchmark sets, which include unimodal, multi-modal, expanded and hybrid composition functions. The performance of SMBO is compared with several genetic algorithms (GAs); the first benchmark compares it with nine codes of traditional/classic GAs, and the second compares SMBO with two recent variants of genetic algorithms. The results show that SMBO performs as well as or better than the GAs in both comparisons. The method is demonstrated on a nonlinear model for management of a Water Supply System (WSS), and the results are compared to the commercial GA toolbox of MATLAB.