摘要
针对鲸鱼优化算法容易陷入局部极值和收敛速度慢的问题,提出了一种结合自适应权重和模拟退火的鲸鱼优化算法.通过改进的自适应权重策略来调整算法的收敛速度,通过模拟退火增强鲸鱼优化算法的全局寻优能力.仿真实验中计算了18个测试函数,对比了粒子群算法、海豚回声定位算法和标准鲸鱼算法并进行统计分析,同时比较了单独结合自适应权重和模拟退火对鲸鱼优化的影响,结果表明,改进的算法在测试函数的极值计算中,计算精度和收敛速度方面都有了明显提升,验证了改进算法的有效性.
Aiming at the problem that whale optimization algorithm is easy to fall into local extreme value and slow convergence speed,this paper proposes a whale optimization algorithm based on adaptive weight and simulated annealing.The improved convergence weight strategy is used to adjust the convergence speed of the algorithm,and the global optimization ability of the whale optimization algorithm is enhanced by simulated annealing.In the simulation experiment,18 test functions were calculated and the genetic algorithm,the particle swarm optimization algorithm and the standard whale algorithm were compared and statistically analyzed.At the same time,the influence of the adaptive weight and simulated annealing on the whale optimization is compared.The results show that the improved algorithm has a significant improvement in the calculation of the extremum of the test function,and the effectiveness of the improved algorithm is verified.
作者
褚鼎立
陈红
王旭光
CHU Ding-li;CHEN Hong;WANG Xu-guang(61477 Bridge and Institute of Electronic Countermeasure,National University of Defense Technology,Hefei,Anhui 230037,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2019年第5期992-999,共8页
Acta Electronica Sinica
基金
国家自然科学基金(No.61571446)
安徽省自然科学基金(No.KY13C152)
关键词
智能优化算法
鲸鱼优化算法
自适应权重
模拟退火算法
intelligent optimization algorithm
whale optimization algorithm(WOA)
adaptive weight
simulated annealing(SA)
