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內容摘要： Constrained
optimization (CO) problems are very important in that they
frequently appear in the real world. A CO problem, in which
both the function and constraints may be nonlinear, consists
of the optimization of a function subject to constraints.
Constraint handling is one of the major concerns when
solving CO problems with particle swarm optimization
combined with Nelder-Mead simplex search method (NM-PSO).
This paper proposes embedded constraint handling methods,
which include the gradient repair method and constraint
fitness priority-based ranking method, as a special operator
in NM-PSO for dealing with constraints. Experiments, which
use 13 benchmark problems, are done and those results of NM-PSO
are compared with the best known solutions reported in the
literature. The results of comparison with three different
meta-heuristics demonstrated that NM-PSO with the embedded
constraint operator is proved to be extremely effective and
efficient at locating optimal solutions. |