چکیده: (124 مشاهده)
The Newton Metaheuristic Algorithm (NMA) is a population-based optimizer whose search is based on the Newton method. Instead of using derivative information, it estimates the local behavior of the objective function from neighboring solutions. Although it has this advantage, the NMA suffers from some shortcomings, including premature convergence and the possibility of being trapped in a local optimum, as well as starting from a random population without prior knowledge. To alleviate these handicaps, the Improved Newton Metaheuristic Algorithm (INMA) incorporates two efficient devices. The first device is a structural initialization scheme (SIS) that improves the initialization phase by generating a set of targeted solutions across the search space. The second device is a statistical regeneration mechanism (SRM) that is incorporated in the cyclic body of the algorithm and regenerates the solutions based on the statistical results of the population. The proposed devices provide a good balance between the exploration and the exploitation capability of the algorithm and reduce the probability of being trapped in a local optimum. In this study, the efficiency of the INMA is demonstrated through the discrete sizing optimization of three large-scale double-layer grids, namely a 520-bar, an 800-bar, and a 1520-bar grid. The design variables are the cross-sectional areas of the members, which are selected from a discrete list of steel pipe sections, and the displacement, stress, and slenderness constraints are imposed according to the AISC-LRFD code. INMA is compared to ECBO, MDVC-UVPS, EDO, ERao-1, ERao-2, and PSO-SRM. The optimization results reveal the competence and the robustness of the INMA for the optimal design of large-scale double-layer grids.
نوع مطالعه:
پژوهشي |
موضوع مقاله:
Optimal design دریافت: 1405/5/2 | پذیرش: 1405/7/7 | انتشار: 1405/7/12