An Improved Differential Evolution Based on Gaussian Disturbance for Multi-objective Optimization

This paper presents an improved differential evolution based on Gaussian disturbance for multi-objective optimization. Differential evolution algorithm is often trapped in local optima and converges slowly. In this paper, Gaussian disturbance is employed to increase the variety of the individual to improve its performance. External archive is employed to reserve the non-dominated solutions and crowding-distance calculation is introduced in order that the solutions in the neighborhood of the solutions with smallest and largest function values or locating in a lesser crowded region will have higher probability to be preserved. The improved differential evolution algorithm is tested on several classical multi-objective optimization benchmark functions. The simulation results show that the improved algorithm can obtain the better solutions and they are widely spread on the true Pareto optimal front.