This study introduces a new search method for box-constrained optimization problems called the search method for box optimization (SMBO). SMBO is a population heuristic-based search methodology that solves global optimization problems. SMBO represents the population as a 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 two benchmark sets, which include unimodal, multimodal, 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 with the commercial GA toolbox of matrix laboratory (MATLAB).
|Number of pages||9|
|Journal||Journal of Water Resources Planning and Management - ASCE|
|State||Published - Nov 2012|
- Evolutionary algorithms
- Genetic algorithms
- Global optimization
- Search methods
- Water supply systems.
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Geography, Planning and Development
- Water Science and Technology
- Management, Monitoring, Policy and Law