Space–Time Concept in Social Network Search Algorithm

Abstract

AbstractNowadays, social networks have become popular platforms in which users can interact with each other virtually. The relationship among users, their influence on each other, and similarity in their behavior are the main features of users in the social networks. These features are utilized in the development of an optimization method, which is called the Social Network Search (SNS) algorithm. The SNS method is a recently developed optimizer, which models the treatment of users in expressing their new views. Four novel optimization operators with new formulations are invented, which are called decision moods. These moods are named imitation, conversation, disputation, and innovation, which are real-world behaviors of users in social networks and model how users are affected and motivated to share their new views. This chapter embeds the concept of space–time in the implementation process of the SNS algorithm to developing space–time social network search (STSNS) algorithm. Single-objective, bound-constraint benchmark problems of IEEE congress on evolutionary computation 2014 (CEC 2014) are utilized to study the efficiency of the STSNS algorithm in solving challenging optimization problems. In addition, the results of the STSNS method is compared with a variety range of optimization methods, including eight state-of-the-art, eight popular, and eight novel methods from the literature. Two well-known non-parametric statistical methods, Friedman and Wilcoxon signed-rank, are utilized to analyze the performance of the developed method, and results demonstrate its superiority in dealing with most of the selected complex optimization problems.KeywordsMetaheuristicOptimization algorithmSocial network search