返回
Spiral Motion Mode Embedded Grasshopper Optimization Algorithm: Design and Analysis
DOI:10.1109/ACCESS.2021.3077616.png)
摘要
En 中文
A new enhanced grasshopper optimization algorithm (GOA) has been developed and successfully applied to feature selection. GOA, as a heuristic algorithm, is proposed by simulating the living habits of grasshoppers in nature. Although GOA has an excellent global optimization capability, it still faces the disadvantage of low efficiency of searching optimization due to its ease of falling into the local optimum. Hence, based on the original GOA, this study integrates new ideas to reduce the defects to obtain a better global optimization ability. Because of the continuous optimization problem, the features of pursuing the best possible individual of spiral motion have been considered. The spiral motion is integrated into the GOA exploitation search stage, which further expands the diversification and intensification trends' capacities and effectively balances the exploration and exploitation procedures. Intuitively speaking, GOA with spiral search method can find better solutions in the exploration movement process, which is more efficient than the original search method. In the experimental comparison, to verify the proposed SGOA's ability in dealing with global unconstrained and constrained optimization problems, we compared it with other 30 IEEE 2017 benchmark tasks in meta-heuristic algorithms. Then, it is adopted to optimize engineering design and feature selection problems. We can know that the proposed SGOA has a good optimization ability in practical application from the experimental results. Spiral motion mode can significantly improve the original GOA's exploitation and exploration ability, and the proposed SGOA is of great assistance in practical fields. More info about this paper can be found on the web services https://aliasgharheidari.com.
Keyword:
Optimization
Spirals
Heuristic algorithms
Feature extraction
Gravity
Licenses
Information technology
Grasshopper optimization algorithm
spiral motion
engineering design problems
feature selection
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Binary dragonfly optimization for feature selection using time-varying transfer functions使用时变传递函数进行特征选择的二进制蜻蜓优化
Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies多种群差分进化辅助Harris hawks优化: 框架与案例研究


