TY - GEN
T1 - Pareto Dominance-based MOEA with Multiple Ranking methods for Many-objective Optimization
AU - Palakonda, Vikas
AU - Ghorbanpour, Samira
AU - Mallipeddi, Rammohan
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Pareto Dominance-based Multi-objective Evolutionary algorithms (PDMOEAs) have issues while handling many-objective optimization problems (MaOPs) due to the lack of selection pressure provided by the Pareto dominance to guide the search process towards the convergence. Hence, most of the PDMOEAs proposed rely on additional selection criterion to establish preferences between the solutions. In this paper, we propose a PDMOEA with multiple ranking methods (PDMOEAMR), an extension to the proposed PDMOEA with ranking methods for MaOPs which assigns priority rank based upon Ranking methods and niche radius. In PDMOEA with ranking methods for MaOPs, ranking methods such as Average rank (AR) in PDMOEA-AR, and weighted sum of objectives (WS) in PDMOEA-WS, are used. Instead of using two ranking methods separately, in the proposed PDMOEA-MR, both the ranking methods AR and WS are incorporated into a common framework and a different strategy is adopted to assign priority rank. The performance of proposed method is analyzed on 16 test problems.
AB - Pareto Dominance-based Multi-objective Evolutionary algorithms (PDMOEAs) have issues while handling many-objective optimization problems (MaOPs) due to the lack of selection pressure provided by the Pareto dominance to guide the search process towards the convergence. Hence, most of the PDMOEAs proposed rely on additional selection criterion to establish preferences between the solutions. In this paper, we propose a PDMOEA with multiple ranking methods (PDMOEAMR), an extension to the proposed PDMOEA with ranking methods for MaOPs which assigns priority rank based upon Ranking methods and niche radius. In PDMOEA with ranking methods for MaOPs, ranking methods such as Average rank (AR) in PDMOEA-AR, and weighted sum of objectives (WS) in PDMOEA-WS, are used. Instead of using two ranking methods separately, in the proposed PDMOEA-MR, both the ranking methods AR and WS are incorporated into a common framework and a different strategy is adopted to assign priority rank. The performance of proposed method is analyzed on 16 test problems.
KW - Convergence
KW - Diversity
KW - Evolutionary Algorithms
KW - Many-objective optimization problems
KW - Pareto dominance
KW - Ranking methods
UR - https://www.scopus.com/pages/publications/85062769412
U2 - 10.1109/SSCI.2018.8628723
DO - 10.1109/SSCI.2018.8628723
M3 - Conference contribution
AN - SCOPUS:85062769412
T3 - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
SP - 958
EP - 964
BT - Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
A2 - Sundaram, Suresh
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
Y2 - 18 November 2018 through 21 November 2018
ER -