关键词: 回溯搜索算法, 约束优化问题, 变异尺度系数, 选择策略, Metropolis准则

Abstract: The Backtracking Search Optimization Algorithm（BSA） is an evolution algorithm based on population. The algorithm has good global search ability. However, it has the shortcoming of low convergence speed. Aiming at the shortcoming, an improved backtracking search optimization algorithm with self-adaptable mutation scale factor and hybrid selection strategy is proposed. The modified mutation scale factor, which may self-adaptable decrease in overall trend, is based on the Metropolis criterion. The modified selection strategy is a hybrid between the whole [q]% priority selection method and tournament selection method. In the selection process, a certain percentage of outstanding individuals are given priority to enter the next generation, and the rest individuals are counterpointed to select the individuals with higher fitness. The simulation experiments on 5 complex constrained optimization problems are performed by the improved algorithm. The experimental results are compared with those of original algorithm and other similar algorithms. Statistical results show that the improved algorithm has effectiveness and competitiveness.

Key words: backtracking search optimization algorithm, constrained optimization problems, mutation scale factor, selection strategy, Metropolis criterion