Large-scale global optimization via swarm intelligence

Abstract

Large-scale global optimization (LSGO) is a challenging task with many scientific and engineering applications. Complexity, nonlinearity and size of the problems are the key factors that pose significant challenges in solving such problems. Though the main aim of optimization is to obtain the global optimal solutions with the least computational costs, it is impractical in most applications. Thus, a practical approach is to search for suboptimal solutions and good solutions, which may not be easily achievable for large-scale problems. In this chapter, the challenges posed by LSGO are addressed, followed by some potential strategies to overcome these difficulties. We also discuss some challenging topics for further research.