混沌映射与t-分布变异策略改进的海鸥优化算法

针对海鸥优化算法(SOA)求解精度较低、迭代后期收敛速度慢、易陷入早熟收敛的缺点,提出一种基于混沌映射和t-分布变异改进的海鸥优化算法(CtSOA),采用tent映射策略使初始海鸥种群均匀分布在搜索空间中,采用t-分布变异策略平衡算法的探索和开发能力,综合两种改进策略提高了算法的全局搜索精度和跳出局部极值的能力。在14个测试函数上分别与SOA、其他五种元启发式算法、单一策略改进的SOA以及其他学者改进的SOA进行对比,实验结果表明,综合两种改进策略的CtSOA具有更优的收敛精度和更快的收敛速度。

Improved seagull optimization algorithm based on chaotic map and t-distributed mutation strategy

In order to solve the shortcomings of seagull optimization algorithm(SOA), such as low precision, slow convergence rate in the later iteration, and fall into premature convergence easily. This paper proposed an improved seagull optimization algorithm(CtSOA), which combined chaotic map strategy and t-distribution mutation strategy. Tent map strategy made the initial seagull population evenly distributed in the search space, and t-distribution mutation strategy balanced the exploration and development capability of the algorithm. Combining the two strategies, CtSOA has higher global search accuracy and stronger ability to jump out of local extremum. Comparing with the SOA, other five meta-algorithms, the single strategy improved SOA, and other scholars improved SOA on 14 test functions, the experimental results show that CtSOA with two improved strategies has better convergence precision and faster convergence speed.