BIEA: A novel evolutionary algorithm for nonlinear constrained programming

Nonlinear constrained problem has been deemed as a hard problem. This paper proposes a kind of evolutionary algorithm for constrained programming. The constrained conditions are converted into an objective and then the constrained programming is transformed into a special bi-objective unconstrained problem. The Pareto concept of multiobjective programming is introduced, then crossover operator using uniform designing method and feasible mutation operator are designed to solve this kind of bi-objective unconstrained programming. The detailed procedure of the algorithm based on two objectives is proposed. Five standard benchmarks are applied to verify the validity of the algorithm. The feasibility and efficiency of the proposed algorithm are shown by comparing with other two algorithms.