A New Adaptive Penalty Function Based Algorithm for Solving Constrained Optimization Problems

A new adaptive penalty function is presented to solve constrained optimization problems.With a simple structure and fewer parameters,the method appropriately balances the objective function and the constraint violations according to the proportion of feasible solutions in the current population.By integrating the presented method with a simple evolutionary strategy,a novel constrained optimization evolutionary algorithm is derived.The new method is tested on several benchmark test functions.The experimental results show that the new method is an effictive and robust approach for solving different constrained optimization problems,and it outperforms or performs similarly to other algorithms.