Good Lattice Swarm Algorithm for Constrained Engineering Design Optimization

Engineering optimization in the intelligence swarm remains to be a challenge. Recently, a novel optimization method based on number-theory and particle swarm, good lattice swarm optimization algorithm (GLSO), is introduced, which intends to produce faster and better global search ability and more accurate convergence because it has a solid theoretical basis. In this paper, four models of constructing good point set are introduced and the GLSO based on new models is rewritten. Some applications of the new model on constrained engineering via employing a penalty function approach suggest that the presented algorithm is potentially a powerful search technique for solving complex engineering design optimization problems.