A Self Adaptive Penalty Function Based Algorithm for Constrained Optimization

This paper proposes a self adaptive penalty function for solving constrained optimization problems using genetic algorithms. In the proposed method, a new fitness value, called distance value, in the normalized fitness-constraint violation space, and two penalty values are applied to infeasible individuals so that the algorithm would be able to identify the best infeasible individuals in the current population. The method aims to encourage infeasible individuals with low objective function value and low constraint violation. The number of feasible individuals in the population is used to guide the search process either toward finding more feasible solutions or toward finding the optimum solution. The proposed method is simple to implement and does not need parameter tuning. The performance of the algorithm is tested on 13 benchmark functions in the literature. The results show that the approach is able to find very good solutions comparable to other state-of-the-art designs. Furthermore, it is able to find feasible solutions in every run for all of the benchmark functions.