SETNDS: A SET-Based Non-Dominated Sorting Algorithm for Multi-Objective Optimization Problems

• Published in: Applied sciences Vol. 10; no. 19; p. 6858
• Main Authors: Xue, Lingling, Zeng, Peng, Yu, Haibin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2020
• Subjects: Algorithms; Authorship; Comparative analysis; Computer applications; dominant set; Efficiency; Evolutionary algorithms; Genetic algorithms; Methods; Multiple objective analysis; Objectives; Optimization; Pareto optimization; Population; Population number; Run time (computers); set theory; SET-based non-dominated sorting algorithm (SETNDS), time complexity; Sorting algorithms; Sorting software; Stadiums; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10196858

Non-dominated sorting, used to find pareto solutions or assign solutions to different fronts, is a key but time-consuming process in multi-objective evolutionary algorithms (MOEAs). The best-case and worst-case time complexity of non-dominated sorting algorithms currently known are O(MNlogN) and O(MN2); M and N represent the number of objectives and the population size, respectively. In this paper, a more efficient SET-based non-dominated sorting algorithm, shorted to SETNDS, is proposed. The proposed algorithm can greatly reduce the number of comparisons on the promise of ensuring a shorter running time. In SETNDS, the rank of a solution to be sorted is determined by only comparing with the one with the highest rank degree in its dominant set. This algorithm is compared with six generally existing non-dominated sorting algorithms—fast non-dominated sorting, the arena’s principle sort, the deductive sort, the corner sort, the efficient non-dominated sort and the best order sort on several kinds of datasets. The compared results show that the proposed algorithm is feasible and effective and its computational efficiency outperforms other existing algorithms.