TY - GEN
T1 - Nondominated sorting based on sum of objectives
AU - Palakonda, Vikas
AU - Pamulapati, Trinadh
AU - Mallipeddi, Rammohan
AU - Biswas, Partha P.
AU - Veluvolu, Kalyana Chakravarthy
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - In Pareto-Dominance based multi-objective evolutionary algorithms (PDMOEAs), nondominated sorting (NDS) procedure plays significantly important role in mating and environmental selection of the solutions. However, NDS is computationally expensive as each solution needs to be compared on all objectives with all solutions in the population. Therefore, the complexity increases with increase in the number of objectives (M) and the number of individuals in the population (N). Therefore, designing an efficient NDS algorithm plays a prominent role in reducing the computational complexity of the MOEAs. In this paper, we propose a NDS algorithm that assigns solutions to the fronts in the ascending order of sum of objectives; by comparing with only solutions that are better than the respective solution in the sum of objectives. To demonstrate the effectiveness, we compared the proposed method with the existing efficient nondominated sorting (ENS) algorithm and Corner Sort (CS). The experimental results indicate the effectiveness of the proposed method in reducing the number of comparisons and runtimes as the number of objectives increases.
AB - In Pareto-Dominance based multi-objective evolutionary algorithms (PDMOEAs), nondominated sorting (NDS) procedure plays significantly important role in mating and environmental selection of the solutions. However, NDS is computationally expensive as each solution needs to be compared on all objectives with all solutions in the population. Therefore, the complexity increases with increase in the number of objectives (M) and the number of individuals in the population (N). Therefore, designing an efficient NDS algorithm plays a prominent role in reducing the computational complexity of the MOEAs. In this paper, we propose a NDS algorithm that assigns solutions to the fronts in the ascending order of sum of objectives; by comparing with only solutions that are better than the respective solution in the sum of objectives. To demonstrate the effectiveness, we compared the proposed method with the existing efficient nondominated sorting (ENS) algorithm and Corner Sort (CS). The experimental results indicate the effectiveness of the proposed method in reducing the number of comparisons and runtimes as the number of objectives increases.
KW - computational complexity
KW - nondominated sorting
KW - number of comparisons
KW - sum of objectives
UR - https://www.scopus.com/pages/publications/85046164472
U2 - 10.1109/SSCI.2017.8280950
DO - 10.1109/SSCI.2017.8280950
M3 - Conference contribution
AN - SCOPUS:85046164472
T3 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
SP - 1
EP - 8
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Y2 - 27 November 2017 through 1 December 2017
ER -