TY - GEN
T1 - Divide-and-Conquer Approach for Revealing the Non-dominated Solutions in Multi-objective Optimization Problem
AU - De, Sagar S.
AU - Dehuri, Satchidananda
AU - Cho, Sung Bae
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/7/31
Y1 - 2018/7/31
N2 - Revealing the non-dominated solutions is one of the vital and essential part of multi-objective optimization algorithms. However, the process of identifying non-dominated solutions in the case of multi-objective optimization problems is computationally very expensive even though properties of dominance relation adhered very strictly. In many multi-objective optimization problems, to identify all non-dominated solutions from a set of N solutions with K objectives naïve and slow approach is commonly accepted with O(KN2) computation time. Realizing this problem as the heart of multi-objective optimization problem, we attempt here a new effective and comparatively better approach for revealing all non-dominated solutions based on divide-and-conquer technique by enjoying O(K N log2 N) computation time. Our experimental results in many benchmark multi-objective optimization problems confirm the aforementioned claim.
AB - Revealing the non-dominated solutions is one of the vital and essential part of multi-objective optimization algorithms. However, the process of identifying non-dominated solutions in the case of multi-objective optimization problems is computationally very expensive even though properties of dominance relation adhered very strictly. In many multi-objective optimization problems, to identify all non-dominated solutions from a set of N solutions with K objectives naïve and slow approach is commonly accepted with O(KN2) computation time. Realizing this problem as the heart of multi-objective optimization problem, we attempt here a new effective and comparatively better approach for revealing all non-dominated solutions based on divide-and-conquer technique by enjoying O(K N log2 N) computation time. Our experimental results in many benchmark multi-objective optimization problems confirm the aforementioned claim.
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U2 - 10.1109/ICIT.2017.45
DO - 10.1109/ICIT.2017.45
M3 - Conference contribution
AN - SCOPUS:85051581454
SN - 9781538629246
T3 - Proceedings - 2017 International Conference on Information Technology, ICIT 2017
SP - 143
EP - 151
BT - Proceedings - 2017 International Conference on Information Technology, ICIT 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Conference on Information Technology, ICIT 2017
Y2 - 21 December 2017 through 23 December 2017
ER -