计算机科学
水准点(测量)
职位(财务)
引力搜索算法
趋同(经济学)
分割
算法
图像分割
人工智能
搜索算法
人口
社会学
人口学
经济
粒子群优化
经济增长
地理
大地测量学
财务
作者
Anjing Guo,Yirui Wang,Lijun Guo,Rong Zhang,Yang Yu,Shangce Gao
标识
DOI:10.1016/j.engappai.2023.106040
摘要
Gravitational search algorithm is a population-based optimization method. To address its low search performance and premature convergence, a novel variant called adaptive position-guided gravitational search algorithm is proposed. It utilizes the best, worst and other particles’ position information to adaptively determine the Kbest particles which provide a good movement direction. The gravitational force is reinforced by Kbest particles and new constructed Dbest particles to improve the exploration and exploitation abilities. Various particles’ position information jointly provide the effective search guideline and accelerate the convergence rate. Validations are conducted to firstly discuss the parameters and strategies of the proposed algorithm. Then, compared with several state-of-the-art gravitational search algorithm variants on CEC2017 benchmark functions, the proposed algorithm proves its superiority. Finally, the proposed algorithm exhibits the good segmentation effect on image threshold segmentation problems.
科研通智能强力驱动
Strongly Powered by AbleSci AI