Computational swarm strategies for single objective design optimization problems

In this paper, three distinct swarm strategies for the optimization of engineering design problems with solitary objectives are presented. These strategies build upon the swarm algorithm of Ray et al. [1] by incorporating new processes that improve the performance of their predecessor. The Constraint Matching strategy calls for the mating of solutions based on constraint satisfaction characteristics. Local Search entails the thorough exploration of regions in parametric space where good solutions potentially reside. Migrating Leaders prescribes the exchange of information between the best performing members of the swarm. As proof of their utility, the strategies were used to solve two well-studied optimal engineering design problems. Solutions obtained by the strategies compare favorably against those of other optimization approaches documented in the literature.