Backtracking Search Optimization Algorithm with Low-Discrepancy Sequences for Mechanical Design Optimization Problems
Qiu Yan Xu · Applied Mechanics and Materials · 2014
This paper presents the backtracking search optimization algorithm with low-discrepancy sequences to solve mechanical design optimization problems involving problem-specific constraints and many different variables. Similar to other evolutionary algorithms, backtracking search optimization algorithm is sensitive to the initial population. Generally speaking, since there is no information about the optimization problem, the initial population should be created uniformly. The low-discrepancy sequences are employed to increase the uniformity of the initial population. The benchmark problems widely used in the literature of mechanical design optimization are used to evaluate the performance of the presented algorithm. Results show that the proposed algorithm is effective and efficient for solving the mechanical design optimization problems considered.