Hybrid Genetic Algorithm Based on the Modification to the New Version of the Price’s Algorithm for Constrained Optimization Problems

The modification to the new version of the Price’s algorithm can find the global minimum of a multimodal, multivariate and nondiffrentiable function. In this paper, the modification to the new version of the Price’s algorithm is taken as a local search operator, which is combined with the real- coded genetic algorithm. Thus a new hybrid genetic algorithm is proposed for constrained optimization problems. The new approach is capable of enhancing the global search ability of the genetic algorithm, improving the accuracy of the minimum function value, as well as reducing the computational burden. The hybrid genetic algorithm has been tested on 13 constrained benchmark problems. The results obtained have been compared with those of other existing algorithms. Simulation results show the effectiveness of the proposed algorithm.