【摘要】 针对鲸鱼优化算法(WOA)收敛速度慢、收敛精度低的问题,在提升性能的基础上保留WOA的简单性,提出一种改进的WOA。利用分段Logistic混沌映射产生混沌序列对种群位置进行初始化,以维持全局搜索时初始种群的多样性。考虑算法的非线性优化过程和搜索过程中个体状态的差异性,在WOA中引入非线性自适应权重策略,以协调全局探索和局部开发能力。通过仿真测试比较改进算法和WOA在求解6个典型基准函数时的性能,实验结果表明,改进算法在寻优过程中能够保持初始种群多样性,且具有更快的收敛速度和更优的收敛精度。更多还原

【Abstract】 To overcome the slow speed and low precision in convergence of the Whale Optimization Algorithm(WO A), to preserve the simplicity of the original algorithm while enhancing the performance, an improved WO A is proposed. Firstly, to maintain the diversity of the initial population in the global search, the population position is initialized by the chaotic sequence generated by the piecewise Logistic chaotic mapping. Secondly, considering the nonlinear optimization process of the algorithm and the difference of individual state in the search process, a nonlinear adaptive weighting strategy is introduced in the basic algorithm to coordinate the global exploration and local development. By the simulation, it compares the performance of the improved algorithm and the WO A on solving six typical benchmark functions. Experimental results show that the improved WOA preserves the initial population diversity in the process of optimization with better convergence speed and precision.更多还原