一种改进的非洲秃鹫算法

An Improved African Vulture Algorithm

针对非洲秃鹫优化算法(African Vultures Optimization Algorithm,AVOA)收敛速度慢、种群多样性损失、易陷入局部最优的缺点,提出一种改进的非洲秃鹫优化算法(Improved African Vulture Algorithm,IAVOA)。首先,在全局探索阶段融入正余弦算法思想同时引入非线性惯性因子协调算法全局和局部的开掘能力,并加快收敛速度。其次,引入自适应t分布和差分变异,拓展局部探索能力,使种群中陷入局部极值的个体有能力跳出陷阱继续搜索。最后,通过基准函数对算法的各项性能指标进行评估。结果表明改进策略能够克服AOVA的不足,提升算法的收敛速度、寻优精度和寻优稳定性。同时,将IAVOA应用于两个电力系统经济调度问题取得了令人满意的成果,进一步验证了IAVOA在实际寻优问题上应用的可行性。

Aiming at the disadvantages of slow convergence speed,loss of population diversity and easily falling into local optimization of African vulture optimization algorithm(AVOA),a Multi-strategy improved African vulture algorithm(IAVOA)is proposed.Firstly,the idea of sine cosine algorithm is introduced in the global exploration stage as well the nonlinear inertia factor to coordinate the global and local mining ability of the algorithm.Secondly,adaptive t-distribution and differential mutation are introduced to expand the local exploitation ability,so that the in-dividuals trapped in local extremum in the population can escape the trap and continue searching.Finally,the per-formance of the algorithm is tested by benchmark function.The results show that the improved strategy can overcome the shortcomings of AOVA and improve the convergence speed,optimization accuracy,and optimization stability of the algorithm.At the same time,IAVOA is applied to two economic dispatch problems of power systems,which verifies the feasibility of applying IAVOA to practical optimization problems.