Non-parameter Penalty Function Multi-Objective Orthogonal Genetic Algorithm for Nonlinear Programming Problem
Liu Chun-an, Yuping Wang · Yunchou yu guanli · 2006
Penalty functions are often used in constrained optimization,but it is difficult to choose parameter property.In this paper,a new non-parameter penalty function multi-objective orthogonal genetic algorithm is presented to solve the nonlinear programming problem.It puts penalty to constraint violations in order to keep a ratio of infeasible solutions in population.As a result,it can not only increase the diversity of population but also avoid the defects of over-penalization.This makes the group approach optimal solution easy.The numerical experiment shows that this algorithm is effective in dealing with the nonlinear programming problem.