Constrained evolution algorithm based on adaptive differential evolution

Solving constrained optimisation is widely used in science and engineering, but the slow convergence speed and premature are the biggest problems researchers face. Research on a constrained evolution algorithm (CO-JADE) based on adaptive differential evolution (JADE) for solving the constrained optimisation problems is proposed in this paper. According to features of the Gaussian distribution, the Cauchy distribution and the mutation factor, we exploited the crossover probability of each individual to improve the search strategy. Aimed to effectively evaluate the relationship between the value of the objective function and the degree of constraint violation, the paper used an improved adaptive tradeoff model to evaluate the individuals of the population. This tradeoff model used different treatment scheme for different stages of the population and implemented on night standard test functions. The experimental shows that the CO-JADE has better accuracy and stability than the COEA/ODE and the HCOEA.