New Multi-objective Genetic Algorithm for Nonlinear Constrained Optimization Problems
Liu Chun-an · 2007
A new approach is presented to solve the nonlinear constrained optimization problem. It neither uses any penalty function nor distinguishes the feasible solutions and the infeasible solutions. Firstly, the constrained optimization problem is transformed into a bi-objective optimization problem. One objective is the objective function of the original nonlinear constrained optimization problem, and the other is the scalar constraints violation. Based on the dominating relation of the Pareto, a new choosing strategy is first designed, and then by combining the choosing strategy with the reasonable design of the genetic operation and different parameters, a new genetic algorithm is finally proposed. The numerical experiment shows that the algorithm is effective in dealing with the nonlinear constraint optimization problem.