title: Swarm intelligence for mixed-variable design optimization.
abstract: Many engineering optimization problems frequently encounter continuous variables and discrete variables which adds considerably to the solution complexity. Very few of the existing methods can yield a globally optimal solution when the objective functions are non-convex and non-differentiable. This paper presents a hybrid swarm intelligence approach (HSIA) for solving these nonlinear optimization problems which contain integer, discrete, zero-one and continuous variables. HSIA provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution. An approach to handle various kinds of variables and constraints is discussed. Comparison testing of several examples of mixed-variable optimization problems in the literature showed that the proposed approach is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.
authors.affiliation: College of Electrical Engineering, Zhejiang University, Hangzhou 310016, China.
authors.forename: Chuang-Xin
authors.initials: CX
authors.lastname: Guo
authors.forename: Jia-Sheng
authors.initials: JS
authors.lastname: Hu
authors.forename: Bin
authors.initials: B
authors.lastname: Ye
authors.forename: Yi-Jia
authors.initials: YJ
authors.lastname: Cao
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001522:Behavior, Animal / Q000502:physiology*; D032701:Biomimetics / Q000379:methods*; D003198:Computer Simulation; D017076:Computer-Aided Design; D004867:Equipment Design / Q000379:methods*; D005246:Feedback; D008962:Models, Theoretical; D012919:Social Behavior