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version 1.1 (12 KB) by Zhe Yang
A new Nature-inspired optimization algorithm: Aptenodytes Forsteri Optimization algorithm (AFO)


Updated 15 Jan 2021

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Update log.
1. 2021.1.1
Version 1.0
All experiments of the paper are run based on this version, except for the experiments of running time.

2. 2021.1.7
Version 1.1
The runtime experiments of the paper are based on this version
Disadvantages of Version 1.0
(1) Too slow
(2) The total number of evaluations is T*(N+m) after the catastrophe strategy is triggered, and m is the number of times the catastrophe strategy is triggered.
However, this problem will not affect the results of this experiment because the maximum number of iterations of the experiment is 50, and the catastrophe strategy will basically not be triggered.

Updated content of Version 1.1
(1) Optimization based on the advantages of MATLAB. The problem of too slow speed is solved.
The core reason for the excessive slowness was that strategy 2 did not use matrix operations in version 1.0.
Note: In order to use matrix operations, this version updates all individuals of the population when using the third strategy, but calculates the fitness value only for those individuals that are eligible. The total number of evaluations is still T*N.
If you want to rewrite this code in another language, we suggest you refer to AFO1. AFO2 is optimized for MATLAB and may not be suitable for your language.
(2) After using the catastrophe strategy, the current iteration number +1,the total evaluation number reverts to T*N

Author: Zhe Yang
School:University of Manchester

版本 1.0
版本 1.1
注2: 减少了调用结构体的次数,如果最求更快的速度,可进一步改写程序,删除所有的结构体,直接传递变量


Cite As

Zhe Yang (2021). A-new-Nature-inspired-optimization-algorithm-AFO (, GitHub. Retrieved .

Comments and Ratings (1)

Premkumar manoharan

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MATLAB Release Compatibility
Created with R2019b
Compatible with R2016b and later releases
Platform Compatibility
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