Swarm intelligence for mixed-variable design optimization.
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.
Guo
Chuang-Xin
CX
College of Electrical Engineering, Zhejiang University, Hangzhou 310016, China.
Hu
Jia-Sheng
JS
Ye
Bin
B
Cao
Yi-Jia
YJ
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