Improved genetic algorithm for constrained optimization

Genetic Algorithms (GAs) are one of the most popular evolutionary algorithms for solving optimization problems. However, it has been found that GAs performance is inferior to other evolutionary algorithms. In this paper, we introduce an improved genetic algorithm for solving constrained optimization problems with a new multi-parent crossover and a local search technique. The proposed algorithm uses a diversity operator instead of mutation and maintains an archive of good solutions. The algorithm has been tested by solving 13 well-known benchmark problems. The results show that the proposed algorithm performs better than well-known state-of-the-art algorithms with a faster convergence behavior.