Mantis Search Algorithm (MSA)

版本 1.0.1 (8.1 MB) 作者: Reda Mohamed
MSA is a novel bio-inspired algorithm for global optimization and engineering design problems
0.0
(0)
478.0 次下载
更新时间 2023/7/2

查看许可证

Mantis Search Algorithm (MSA) is a novel bio-inspired optimization algorithm that is inspired by the unique hunting behavior and sexual cannibalism of praying mantises. MSA consists of three optimization stages, including the search for prey (exploration), attack prey (exploitation), and sexual cannibalism. Those operators are simulated using various mathematical models to effectively tackle various optimization problems.
MSA is rigorously tested on fifty-two optimization problems and three real-world applications (five engineering design problems, and the parameter estimation problem of photovoltaic modules and fuel cells) to show its versatility and adaptability to different scenarios. To disclose the MSA’s superiority, it is compared to two categories from the rival optimizers: the first category involves well-established and highly-cited optimizers, like Differential evolution; and the second category contains recently-published algorithms, like African Vultures Optimization Algorithm. This comparison is conducted using several performance metrics, the Wilcoxon rank-sum test and the Friedman mean rank to disclose the MSA’s effectiveness and efficiency. The results of this comparison highlight the effectiveness of this new approach and its potential for future optimization application.
The source code of this algorithm is also available at https://github.com/redamohamed8/Mantis-Search-Algorithm-MSA-

引用格式

Reda Mohamed (2024). Mantis Search Algorithm (MSA) (https://www.mathworks.com/matlabcentral/fileexchange/131833-mantis-search-algorithm-msa), MATLAB Central File Exchange. 检索时间: .

MATLAB 版本兼容性
创建方式 R2023a
兼容任何版本
平台兼容性
Windows macOS Linux
标签 添加标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

IterMSA

版本 已发布 发行说明
1.0.1

The MSA's termination condition in this version relies on the maximum number of iterations, not the maximum number of function evaluations.
1.0.0