A parallel global-local mixed evolutionary algorithm for multimodal function optimization

This paper presents a two-level parallel evolutionary algorithm for solving function optimization problems containing multiple solutions. By combining the characteristics of both global search and local search, the former enables individuals to draw closer to each optimal solution and keeps the genetic diversity of individuals. Then different individuals are selected for local evolution in their appropriate neighborhood. This simple as well as easy-to-handle algorithm turns out to be very practical according to the numerical experiments which indicate that all optimal solutions can be found out by running the algorithm once within a fairly short period of time.