Abstract:

As standard Backtracking Search Optimization Algorithm (BSA) has the shortcoming of slow convergence, a new mutation scale factor based on Maxwell-Boltzmann distribution and a crossover strategy with greedy property were introduced to improve it. Maxwell-Boltzmann distribution was used to generate mutation scale factor, which could enhance search efficiency and convergence speed. Mutation population learning from outstanding individuals was adopted in less exchange-dimensional crossover strategy to add greedy property to crossover as well as fully ensure population diversity, which managed to avoid the problem that most existed algorithms easily trap into local minima when added greedy property. The simulation experiments were conducted on fifteen Benchmark functions. The results show that the improved algorithm has faster convergence speed and higher convergence precision, even in the high-dimensional multimodal functions, the improved algorithm's search results are nearly 14 orders of magnitude higher than those of original BSA after the same iterations, and its convergence precision can reach 10-10 or less.

摘要：

针对回溯搜索优化算法(BSA)收敛速度慢的缺点,提出基于麦克斯韦〖CD*2〗玻尔兹曼分布的变异尺度系数和带贪婪性的交叉策略,来提高算法收敛速度。利用麦克斯韦〖CD*2〗玻尔兹曼分布产生变异尺度系数,能有效提高搜索效率,提高收敛速度;在交换维数较少的交叉策略中使用向优秀个体群学习过的变异种群进行交叉,在充分保证种群多样性的前提下为交叉策略添加了一定贪婪性,成功克服了以往算法添加贪婪性时易陷入局部最优的缺点。对15个标准测试函数进行仿真实验,结果显示,改进算法收敛速度较快，收敛精度较高,即使在高维多峰函数中,相同迭代次数后改进算法的搜索结果比原BSA平均高出近14个数量级,收敛精度均达到10-10以上。