  Nonlinear constrained optimization problems are encountered in many
scientific fields. To utilize the huge calculation power of current computers,
many mathematic models are also rebuilt as optimization problems. Most of them
have constrained conditions which need to be handled. Borrowing biological
concepts, a study is accomplished for dealing with the constraints in the
synthesis of a four-bar mechanism. Biologically regarding the constrained
condition as a form of selection for characteristics of a population, four new
algorithms are proposed, and a new explanation is given for the penalty method.
Using these algorithms, three cases are tested in differential-evolution based
programs. Better, or comparable, results show that the presented algorithms and
methodology may become common means for constraint handling in optimization
problems.
