人工蜂群算法
算法
粒子群优化
遗传算法
趋同(经济学)
计算机科学
人工智能
机器学习
经济增长
经济
作者
Bo Ye,Jinping Hu,Wei Zha,Bo Wang,Qiyuan Gao,Fang Guo,Amit Krishna Dwivedi,Sheng Liu
标识
DOI:10.1109/jsen.2023.3336167
摘要
A novel localization method for wireless capsule endoscope (WCE) based on an improved artificial bee colony (IABC) algorithm is presented. This work contributes to improving the two key aspects of the approach, i.e., optimizing the parameters and improving the search strategy. The experimental results achieved in this work are compared with the Levenberg–Marquardt (LM) algorithm, artificial bee colony (ABC) algorithm, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. Compared with the GA algorithm, ABC algorithm and PSO algorithm, the IABC algorithm achieved significant improvement in terms of the convergence speed, convergence time and running time, and average relative error. For instance, the average relative error of the IABC algorithm was reduced by 3.48%, 1.47%, 2.79%, and 1.97%, compared with the GA algorithm, ABC algorithm, PSO algorithm, and LM algorithm, respectively. In this work, experiments were also performed to validate the feasibility and accuracy of the proposed algorithm. Overall, promising results were achieved in this work and outcomes play a significant role in realizing WCE closed-loop active control, biopsy, and administration in the future.
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