Hybrid algorithm for solving constrained optimization problems

A hybrid algorithm based on modified augmented Lagrange function and PSO is proposed for solving constrained optimization problems. The general constrained optimization problem is converted into a bound constrained optimization problem. The basic steps off the proposed hybrid algorithm comprise an outer iteration and an inner iteration. The inner iteration, in which a nonlinear bound constrained minimization sub-problem of the modified augmented Lagrange multiplier, is solved by improved PSO algorithm. The outer iteration is performed to update the Lagrange multipliers and penalty parameters using a first-order update scheme, check for convergence and accordingly reinitiate another bound constrained minimization or declare convergence. The proposed algorithm is tested on 8 well-known benchmark constrained optimization problems, and the results show that it is very suitable and steadier than other algorithms from the literature for different constrained optimization problems.