Introduction and Benchmark Result Comparison of Social-Inspired Algorithms

In the field of research and development, the most valuable source of inspiration comes from nature itself. Animals, insects, microorganisms exhibit some working principles that inspire today's science and to advance and present ideas in this field. These principles are called bio-inspired algorithms. Of course, in the years when a lot of diversity exploded in the suggestions of bio-inspirational algorithms, it did not take long to introduce new techniques, the concept of which is a very special animal; human. And these techniques are known as socio-inspired algorithms that inspire their functioning in behaviors based on human species and can be applied to solve any problems. Socio-inspired algorithms are a particular type of metaheuristic that bases its behavior on human society. It is a simple approach to the complex world of evolutionary computing, with multiple simulations with recognizable patterns day by day. The aim of this study is to check whether these algorithms have the functional capacity to solve optimization problems. Through experimental analysis, the results of these algorithms will be collected for a known benchmark and compared with those of some reference algorithms that allow assessing their potential in this type of problem adequately. According to the results of the comparison, the effectiveness of the algorithms will be evaluated.

Keywords

Socio-Inspired Algorithms, Bio-Inspired Algorithms, Evolutionary Algorithm, Particle Swarm Optimization, Experimental Analysis, Comparative.