Social Network Search Guided by Spherical Evolution

Social network search (SNS) is one of the up-to-date nature-inspired algorithms that modulates optimization behavior by simulating the behavioral and emotional changes of social network users. SNS has proven to be relatively good at handling different types of optimization problems. Spherical search has been one of the more successful search strategies in recent years because it uses a spherical search approach to search a larger solution space and has a good embedding in various algorithms. We propose an improved algorithm SESNS based on these properties, mapping out a better approach and strategy for the hybrid algorithm. To verify the success of this improved algorithm, we choose CEC2017 as the test set and several other metaheuristic algorithms as competitors to demonstrate the effectiveness of our improved method.