Laplacian biogeography-based algorithm using a gaining–sharing knowledge-based strategy for global optimization problems and the Lennard-Jones problem

Hybridizing nature-inspired algorithms helps to overcome the limitations of the individual algorithms. These methods are good at either exploration or exploitation. The balance between these two is a crucial factor in designing a robust and efficient algorithm. In this article, two such algorithms, the Laplacian biogeography-based optimization (LX-BBO) and gaining–sharing knowledge-based (GSK) algorithms, are hybridized to create an algorithm balancing exploitation and exploration, named LX-GSBBO. The proposed algorithm is tested and compared with its counterparts on IEEE CEC 2014 benchmark functions, structural engineering problems and one complex real-world problem, i.e. the Lennard-Jones potential problem. The statistical tests and convergence graphs confirm the superiority of the proposed algorithm.