Convergence of nomadic genetic algorithm on benchmark mathematical functions

Nomadic genetic algorithm is a type of multi-population migration based genetic algorithm that gives equal importance to low fit individuals and adaptively chooses its migration parameters. It has been applied to several real life applications and found to perform well compared to other genetic algorithms. This paper exploits the working of nomadic genetic algorithm (NGA) for benchmark mathematical functions and compares it with the standard genetic algorithm. To compare its performance with standard GA (SGA), the prominent mathematical functions used in optimization are used and the results proved that NGA outperforms SGA in terms of convergence speed and better optimized values.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► Nomadic genetic algorithm (NGA) is an unbiased multi-population genetic algorithm with migration capabilities. ► Improves diversity by giving equal importance to all individuals in the population and leads to a globally optimum solution. ► The migration parameters of multipopulation GA is intrinsically taken care of by NGA and need not be specified explicitly. ► The algorithm has been tested on standard benchmark functions and proved to perform well.