Improved-Aptenodytes-Forsteri-Optimization-IAFO-Algorithm-

Versione 1.0 (3,36 MB) da Zhe Yang
Improved Aptenodytes Forsteri Optimization (IAFO) : Algorithm and applications
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Aggiornato 26 mar 2022

Improved-Aptenodytes-Forsteri-Optimization-IAFO-Algorithm-

Improved Aptenodytes Forsteri Optimization (IAFO) : Algorithm and applications

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文件内容

1.测试函数

算法是原始AFO算法(AFO2,AFO3,不同版本)

测试函数集合是CEC2017测试函数集

2.现实问题

算法有两个

IAFO_final1 用于连续问题的改进IAFO算法,局部搜索策略是拟牛顿法

IAFO_final0 用于离散问题的改进IAFO算法,局部搜索策略是2-opt,该策略极其简单,建议根据实际问题特性,更换其他的离散类邻域搜索方法

求解问题有

工业设计问题

TSP问题

FJSP问题

路径规划问题(栅格地图)

  1. Bunchmark functions

The algorithm is the original AFO algorithm (AFO2,AFO3,different versions)

Bunchmark function set is CEC2017 Bunchmark function set

  1. Real-world problems

There are two algorithms

IAFO_final1 is an improved IAFO algorithm for continuous problems, and the local search strategy is the proposed Newton method

IAFO_final0 is an improved IAFO algorithm for discrete problems, the local search strategy is 2-opt, the strategy is extremely simple, it is recommended to replace other discrete class neighborhood search methods according to the actual problem characteristics

The solved problems are

Industrial design problems

TSP problem

FJSP problem

Path planning problem (raster map)

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Copy right

你可以免费使用本代码库中的所有代码,但是请注明出处并引用相关的参考文献。 You are free to use all the code in this code base, but please give credit and cite the relevant references.

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Paper

Yang Z, Deng L B, Wang Y, et al. Aptenodytes Forsteri Optimization: Algorithm and applications[J]. Knowledge-Based Systems, 2021, 232: 107483.

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作者:杨喆

邮箱:454170989@qq.com

学校:英国曼彻斯特大学

Author:Yang Zhe

E-mail: 454170989@qq.com

School: University of Manchester, UK

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Cita come

Zhe Yang (2024). Improved-Aptenodytes-Forsteri-Optimization-IAFO-Algorithm- (https://github.com/TwilightArchonYz/Improved-Aptenodytes-Forsteri-Optimization-IAFO-Algorithm-/releases/tag/1.0), GitHub. Recuperato .

Yang Z, Deng L B, Wang Y, et al. Aptenodytes Forsteri Optimization: Algorithm and applications[J]. Knowledge-Based Systems, 2021, 232: 107483.

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