A hybrid genetic algorithm integrated with sequential linear programming
Jiang Zheng, Bin Liu, Liankui Dai, WU Tie-jun · 2004
A new hybrid genetic algorithm is proposed for nonlinear programming problems in this paper, which combines a genetic algorithm (GA) with a sequential linear programming method. During the iterative computation process, if the iterative points in the GA do not obtain crossover or mutation operation, the objection function and constraints of these points will be linearized. In order to satisfy the constraints within the neighborhood of these points, soft constraints are added, and the linearized optimization problem can be solved with the linear programming. The new hybrid genetic algorithm is globally convergent; it does not require that the iterative points must be feasible. Simulation results show that the algorithm is effective and reasonable, and it can be widely used in the complicated nonlinear programming.