A new penalty based genetic algorithm for constrained optimization problems
Yibo Hu, Yuping Wang, Fu-Ying Guo · 2005
Penalty functions are often used to handle constraints for constrained optimization problems in evolutionary algorithms. However it is difficult to control penalty parameters. To overcome this shortcoming, a new penalty function with easily-controlled penalty parameters is designed in this paper. The fitness function defined by this penalty function can distinguish feasible and infeasible solutions effectively. Meanwhile, the orthogonal design is used to generate initial population and design crossover operator. Based on these, a new genetic algorithm for constrained optimization problems is proposed. The simulations are made on five widely used benchmark problems, and the results indicate the proposed algorithm is effective.