Adaptive and migration-enhanced tree seed algorithm for multi-threshold CT image segmentation and lung cancer recognition
Chenxi Li, Jianhua Jiang, Zhixing Ma, Zhilong Yu, Hao Li, Jiayi Liu, Lingna Li, Zhenhao Yu, Mahamed G.H. Omran, Mahamed G.H. Omran, Mahamed G.H. Omran, Mahamed G.H. Omran
TL;DR
This paper introduces an improved Tree-Seed Algorithm that enhances optimization performance and achieves better lung cancer recognition in CT images.
Contribution
The paper proposes an Adaptive and Migration-enhanced Tree Seed Algorithm (AMTSA) with novel mechanisms for improved global optimization and stability.
Findings
AMTSA outperformed state-of-the-art optimizers on IEEE CEC 2014 benchmark functions.
AMTSA achieved 89.5% accuracy in lung cancer CT image classification, surpassing other models.
AMTSA demonstrated superior convergence speed and optimization capability in high-dimensional spaces.
Abstract
The Tree-Seed Algorithm (TSA) is a swarm intelligence algorithm inspired by the propagation relationship between trees and seeds. However, the original TSA is prone to premature convergence and becomes trapped in local optima when addressing high-dimensional, complex optimization problems, limiting its practical efficacy. To overcome these limitations, this paper proposes an Adaptive and Migration-enhanced Tree Seed Algorithm (AMTSA), which integrates three key mechanisms to significantly enhance performance in solving complex optimization tasks. First, to effectively evade local optima, an adaptive tree migration mechanism is designed to dynamically adjust the search step-size and direction based on individual fitness, thereby improving global exploration. Second, to enhance the algorithm’s adaptability and efficiency across different search stages, an adaptive seed generation strategy…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Brain Tumor Detection and Classification · Medical Image Segmentation Techniques