Junchen Wang
ABSTRACT
Nondominated sorting (NS) is commonly needed in multi-objective optimization to distinguish the fitness of solutions. Since it was suggested, several NS algorithms have been proposed to reduce its time complexity. In our study, we found that their performances are closely related to properties of the distribution of a data set, especially the number of fronts. To address this issue, we propose a novel NS algorithm Filter Sort. We also propose a new benchmark data generator for evaluating the performance of a NS algorithm. Experimental results show that our algorithm is superior to several state-of-the-art NS algorithms in most cases.
- K. McClymont and E. Keedwell. 2012. Deductive Sort and Climbing Sort: New Methods for Non-Dominated Sorting. Evolutionary Computation 20, 1 (2012), 1--26. Google Scholar
Digital Library
- H. Wang and X. Yao. 2014. Corner Sort for Pareto-Based Many-Objective Optimization. IEEE Transactions on Cybernetics 44, 1 (2014), 92--102.Google ScholarCross Ref
- X. Zhang, Y. Tian, R. Cheng, and Y. Jin. 2018. A Decision Variable Clustering-Based Evolutionary Algorithm for Large-scale Many-objective Optimization. IEEE Transactions on Evolutionary Computation 22, 1 (2018), 97--112.Google ScholarCross Ref
Index Terms
- An efficient nondominated sorting algorithm
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