A novel differential evolution algorithm based on JADE for constrained optimization

Abstract

To overcome the problem of slow convergence and easy to be plunged to premature when the traditional differential evolution algorithm for solving constrained optimization problems, a novel differential evolution algorithm (CO-JADE) based on adaptive differential evolution (JADE) for constrained optimization was proposed. The algorithm used skew tent chaotic mapping to initialize the population, generated the crossover probability of each individual according to the normal distribution and the Cauchy distribution and the mutation factor according to the normal distribution. CO-JADE used improved adaptive tradeoff model to evaluate the individuals of population. The improved adaptive tradeoff model used different treatment scheme for different stages of population, which aimed to effectively weigh the relationship between the value of the objective function and the degree of constraint violation. Simulation experiments were conducted on the night standard test functions. CO-JADE was much better than COEA/ODE and HCOEA in terms of the accuracy and standard variance of final solution. The experimental results demonstrate that the CO-JADE has better accuracy and stability.