An Improved Genetic Algorithm for Constrained Optimization Problems

The mathematical form of many optimization problems in engineering is constrained problems. In this paper, an improved genetic algorithm based on two-direction crossover and grouped mutation proposed to solve addition making full use the direction information parent individual, adds additional search finally searches better two directions, which improves efficiency. divides population into groups uses operators with different properties for each group give play characteristics these improve experiments IEEE CEC 2017 competition real-parameter ten real-world problems, outperforms other state-of-the-art algorithms. Finally, used optimize a single-stage cylindrical gear reducer.