Augmented Lagrange multiplier based fuzzy evolutionary algorithm and application for constrained optimization

Fuzzy two-phase evolutionary programming (FTPEP) is proposed in this paper based on augmented Lagrange multiplier for constrained optimization comparing to the lack of classic evolutionary algorithm applied in nonlinear constrained optimization. FTPEP based on augmented Lagrange multiplier has two steps: the first phase uses the standard fuzzy evolutionary programming to find a near global solution, which is employed in second phase; through the use of augmented Lagrange multiplier in the second phase and by gradually place emphasis on violated constraints in the objective function, the trial solutions are drove to the optimal point. FTPEP has two phases in the global optimization, the algorithm is proved to be reliable and effective, and it is especially employed in heavily nonlinear constraint problems. Computation examples are given in the end of this paper, the simulation result proves the characters of FTPEP, and can be applied in the constrained optimization of robot track planning.