NSM_SFS

Development of the Natural Survivor Method (NSM) for Designing an Updating Mechanism in Metaheuristic Search Algorithms

NSM-SFS is the most powerful enhanced version of the Stochastic Fractal Search (SFS) algorithm. The Natural Survivor Method (NSM) was used in the design of the update mechanism of SFS. NSM-SFS was tested on IEEE CEC 2020 Benchmark Problems and constrained real-world engineering problems. NSM-SFS is one of the most competitive metaheuristic search algorithms in the literature.

For more information about the Natural Survivor Method (NSM) and the details of the NSM-SFS algorithm, please read the article titled "Development of the Natural Survivor Method (NSM) for designing an updating mechanism in metaheuristic search algorithms":

https://www.sciencedirect.com/science/article/abs/pii/S0952197623003056

Build an enhanced version of your meta-heuristic search algorithm using the NSM-based survivor selection mechanism. You can use the NSM method to design the update mechanism in the MHS algorithm. The method is very easy to implement. You can use different strategies in applying the NSM method. In this way, you can design the version of your algorithm that is most effective on your optimization problem. In a way, this can be thought of as fine-tuning, adapting the algorithm for the optimization problem. We introduced alternative switching strategies to implement NSM. We explained them in the article. We recommend using the NSM method in your algorithm designs.

Survivor Selection Methods for Design of Update Mechanism in Meta-heuristic Search Algorithms

(i) Fitness value-based method: traditional survivor-selection method used in MHS algorithms

if (X[inew]_Fitness_Value > X[i]_ Fitness_Value)

P[i]= Xinew (survivor is Xinew) else P[i]= Xi (survivor is Xi) end if

(ii) NSM-score based method: proposed survivor-selection method

if (X[inew]_NSM_Score > X[i]_NSM_Score )

P[i]= Xinew (survivor is Xinew) else P[i]= Xi (survivor is Xi) end if