基于罗盘算子的改进蝙蝠优化算法

Improved Bat Optimization Algorithm Based on Compass Operator

优化问题广泛存在于工程技术、经济管理等各个领域。实际问题的复杂性,导致传统的优化方法难以解决这些问题。随着迭代计算过程的推进,标准蝙蝠算法在进化后期容易陷入局部最优且种群多样性差。虽然目前已有大量工作针对蝙蝠算法的性能进行了改进,但难以同时满足收敛速度与寻优精度的要求。针对这些问题,提出了基于罗盘算子的改进蝙蝠算法,借鉴鸽群优化算法,引入了罗盘算子帮助蝙蝠种群快速找到质量高的个体,提高蝙蝠算法的开发和搜索能力。之后在MATLAB环境下,通过6种经典多维测试函数分别对该算法与遗传算法、标准蝙蝠算法进行仿真对比实验与双侧t检验。结果表明,改进算法的进化效率、优化深度和成功率均得到了较大程度的提升,对工程复杂函数有很大的价值。

Optimization problems widely exist in various fields such as engineering technology and economic management.Due to the complexity of practical problems,traditional optimization methods are difficult to solve these problems.With the advancement of iterative calculation process,the standard bat algorithm is prone to fall into local optimality and poor population diversity in the later stage of evolution.Although the current bat algorithm has done a lot of work in performance improvement,it is difficult to meet the requirements of convergence speed and optimization accuracy.Aiming at these problems,the improved bat algorithm based on compass operator(BACO)was proposed.Based on the pigeon group optimization algorithm,the compass operator is introduced to help the bat population to quickly find high-quality individuals and improve the development and search ability ofbat algorithm.Then in the MATLAB environment,the algorithm is compared with the genetic algorithm and the standard bat algorithm by six classical multi-dimensional test functions.The results show that the evolutionary efficiency,optimization depth and success rate of the improved algorithm are greatly improved,which has great value for engineering complex functions.