Multi-Modal Optimization with k-Cluster Big Bang-Big Crunch

More details and insights into the research paper: https://arxiv.org/abs/2401.06153

k-BBBC is a multi-modal optimization algorithm that works both with large number of optima and high dimensionality. k-BBBC can work on both maximization and minimization problems. k-BBBC features non-elitist and elitist version. In the project, the names of the functions related to k-BBBC have the letter B at the beginning. You can refer to these functions for details about k-BBBC. This project also includes two post-processing methods namely, identification and quantification.Please check the paper for further details on that methods.

Follow these steps to run the k-BBBC:

1. Open the folder in MATLAB.
2. Write this line of code in Command Window:[retrieved,missed,optimalPoints] = RUNNER(funcInput, nGen,elitism, animation,drawPlotOptima)
3. Edit the funcInput variable to set the function to be optimised. Please check B_funcAndSettings().
4. Edit the nGen variable to set the number of generations. It is recommended as 1000. Please check the paper for further details and also RUNNER help.
5. Edit the elitism variable to set the elistim for k-BBBC. It can be 'elitist' or 'non-elitist'.
6. Edit the animation variable to see animation while code running. It can be true or false.
7. Edit the drawPlotOptima variable to see optimal Points after the run. It can be true or false.

For example:

[retrieved, missed, optimalPoints]=RUNNER('key4', 1000,'elitist',true,true)

Check the function help sections for things you don't understand or want to learn the details.

Authors:

• Kemal Erdem Yenin
• Reha Oguz Sayin
• Kuzey Arar
• Kadir Kaan Atalay
• Fabio Stroppa

Reference: K. E. Yenin, R. O. Sayin, K. Arar, K. K. Atalay, and F. Stroppa. Multi-Modal Optimization with k-Cluster Big Bang-Big Crunch Algorithm. (2023) Submitted to IEEE Transactions on Evolutionary Computation (ArXiv).