Abstract: A multi-objective evolutionary algorithm is applied to optimize the face-shovel excavator attachment, which sets the variable quantity of bucket’s cutting angle in excavating along horizontal line, the stick digging force and the bucket digging force in vertical direction in main digging range as optimization goals. Aiming at improving the performance of NSGA-II in the optimization problems of complicated Pareto front, a dynamic sorting algorithm is designed to improve the diversity. Differential evolution operator and Cauchy mutation operator are proposed for improving the convergency and the ability of global optimization. By using a class of continuous multi-objective optimization test instances to test the improved NSGA-II, the experimental results indicate that the proposed algorithm could significantly outperform NSGA-II on these test instances. The proposed algorithm is applied to a practical example of face-shovel excavator, and can make use of TOPSIS to select the most satisfied schedule from the Pareto set. The comparison with other schemes testifies the feasibility and effectiveness of the improved NSGA-II.

Key words: multi-objective optimization, NSGA-II, technique for order preference by similarity to ideal solution, working device, face-shovel excavator