基于Circle映射和自适应t分布变异改进的鹈鹕优化算法

Improved Pelican Optimization Algorithm Based on Circle Mapping and Adaptive t-Distribution Mutation

针对传统鹈鹕优化算法(POA)存在收敛速度慢、易陷入局部最优解等缺陷,提出一种基于Circle映射初始化和自适应t分布变异的改进的鹈鹕优化算法(IPOA)。首先,在种群初始化阶段,采用Circle映射生成具有高度多样性的初始解,并结合反向学习策略,提高种群多样性,增强种群的探索能力。其次,在迭代过程中,采用自适应t分布变异操作对个体进行扰动,有助于鹈鹕优化算法跳出局部最优解并提高收敛速度。另外,在鹈鹕优化算法的探索阶段引入自适应因子和改进惯性权重,更好地平衡算法全局探索能力和局部开发能力。最后,在多个测试函数上将IPOA与其他4种经典算法进行比较。实验结果表明,IPOA在收敛速度、全局搜索能力和收敛鲁棒性方面均有显著提升。

In view of the shortcomings of the traditional pelican optimization algorithm,such as slow convergence speed and easy to fall into local optimal solutions,an improved pelican optimization algorithm based on Circle map initialization and adaptive t distribution mutation is proposed.First,in the population initialization stage,the Circle mapping is used to generate an initial solution with a high degree of diversity,and combined with the reverse learning strategy,the diversity of the population is improved and the exploration ability of the population is enhanced.Secondly,in the iterative process,the adaptive t-distribution mutation operation is used to perturb the individual,which helps the pelican optimization algorithm jump out of the local optimal solution and improve the convergence speed.In addition,an adaptive factor and an improved inertia weight are introduced in the exploration stage of the pelican optimization algorithm,which better balances the global exploration ability and local development ability of the algorithm.Finally,IPOA is compared with other four classical algorithms on several test functions.Experimental results show that IPOA has a significant improvement in convergence speed,global search ability and convergence robustness.