Discussing the article: "Adaptive Social Behavior Optimization (ASBO): Schwefel, Box-Muller Method"
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Check out the new article: Adaptive Social Behavior Optimization (ASBO): Schwefel, Box-Muller Method.
This article provides a fascinating insight into the world of social behavior in living organisms and its influence on the creation of a new mathematical model - ASBO (Adaptive Social Behavior Optimization). We will examine how the principles of leadership, neighborhood, and cooperation observed in living societies inspire the development of innovative optimization algorithms.
There are many examples of group behavior in nature, where living organisms join together in societies to increase their chances of survival and innovation. This phenomenon, observed in the animal kingdom, in human society, and in other forms of life, has become a fascinating subject of study for evolutionary biologists and social philosophers. By studying such societies, a computational model has been developed that simulates their successful functioning with respect to certain goals. These models, such as particle swarm optimization and ant colony optimization, demonstrate the efficiency of group work in solving optimization problems.
This article examines the concept of social structure and its influence on decision-making processes in groups. We also present a mathematical model based on principles of social behavior and interaction in societies that can be applied to achieve global optimization. This model, called ASBO (Adaptive Social Behavior Optimization), takes into account the influence of the environment on group decision making, including leadership, neighborhood, and self-organization. The algorithm was proposed by Manojo Kumar Singh and published in 2013 in "Proceedings of ICAdC, AISC 174" edited by Aswatha Kumar M. et al.
Author: Andrey Dik