生物地理学优化算法(Biogeography-base optimization,BBO)是一种新型的智能算法,因其参数少、易于实现等优点而受到学界的广泛关注和研究,并显示出了广阔的应用前景。为了提高算法的优化性能,对BBO算法提出一种改进。改进的算法在将差分优化算法(Differential evolution,DE)中的局部搜索策略同BBO算法中的迁移策略相结合的基础上,针对迁移算子和变异算子分别做出改进,并通过基准函数的测试证明了改进后的算法在迭代过程中种群进化、寻优能力以及算法的收敛性能得到进一步提升。尝试将改进了的生物地理学优化算法应用于圆柱度误差评定。依据国家标准,结合最小区域法,以圆柱度误差数学模型为目标函数,该算法实现了误差评定优化求解。通过该寻优结果与其他方法的评定结果的比较,验证了该种算法的可行性和正确性及其优越性。
The biogeography-based optimization algorithm(BBO) is a new intelligent algorithm. It receives wide academic attention and research, and showed a broad application in many fields. In order to improve the performance of the algorithm, an improved BBO algorithm is proposed, which is based on the combination of the local search strategy in differential evolution(DE) algorithm and the migration strategy in BBO algorithm. The improvement of the migration operator and mutation operator make the evolution and the optimization of the algorithm much better. Meanwhile, the research attempts to apply the improved biogeography optimization algorithm to the cylindricity error evaluation. The process of optimization combines the minimum zone method, according to the national standard, to achieve the purpose of the optimization of the cylindricity error mathematical model of objective function. The results gotten from experiments on the objective function optimization and the results from other methods are compared, which verifies the feasibility of the method and the correctness and superiority.