Self-adaptive ant colony differential evolution algorithm

Aiming at the defects of low generality and robustness,slow rate of convergence,low accuracy and easily falling into local optimum in the traditional differential evolution algorithm,a self-adaptive and multi-strategy differential mutation based on ant colony optimization algorithm is proposed for high-dimensional problems.According to the pheromone,the individual selects differential operator with roulette selection operator strategy in each generation,and updates the pheromone dynamically based on the contribution of each mutation evolution model.The model which makes a greater contribution will be chosen.Finally,five high-dimensional benchmark functions are used to test this algorithm.Experimental result indicates that the proposed algorithm effectively avoid the premature phenomenon and the slow convergence velocity,while being highly robust and good generality.