关键词: 回溯搜索优化算法, 引导机制, 小生境技术, 变异策略

Abstract: Backtracking Search optimization Algorithm（BSA） is a new optimization algorithm which is proposed in recent years. For the convergence speed of original BSA is slow and it is easily trapped in local optima, an improved BSA based on the combination of guidance with the best individual and niche technique is proposed to improve the global performance of it. In the method, the strategy with learning from the best individual is introduced in the mutation operator of original BSA to improve the convergence speed of it in the first. In the second, a niche repulsing technology is designed in the paper. The niching radius is determined according to the average minimal distance between every individual and the other individuals, and some parts individuals with high similarity are deleted, some new individuals are generated by a new mutation method which is designed with combining the worst information of current generation, and the new individuals are supplemented in the new population to maintain the constant number of population, the diversity of the population is increased by this operation. The convergence speed and the diversity of population is fully considered in the improved BSA, and the performance of the original BSA is largely improved. 10 typical functions are used in the simulation experiments, and the results are compared with those of other algorithms. The results indicate that the improved algorithm has good performance in terms with the convergence speed and accuracy.

Key words: Backtracking Search optimization Algorithm（BSA）, guidance mechanism, niche technology, mutation strategy