Advances in Nature-Inspired Computing and Applications by Shishir Kumar Shandilya & Smita Shandilya & Atulya K. Nagar
Author:Shishir Kumar Shandilya & Smita Shandilya & Atulya K. Nagar
Language: eng
Format: epub
ISBN: 9783319964515
Publisher: Springer International Publishing
Parameter
NSGA-II
SMS-EMOA
MOEA/D
N
210
210
210
G
500
500
500
P c
0.9
0.9
0.9
P m
1/n
1/n
1/n
T
–
–
20
For each instance of the MOCHS, 30 independent runs were performed with each MOEA. The algorithms were evaluated using the and IGD performance indicators. A statistical analysis was carried out overall the runs in the test problem and the performance indicator under consideration.
Since the features of the MOCHS problem are unknown, the reference Pareto front for computing the IGD performance indicator had to be constructed. In our experimental study, the reference Pareto front for each instance of the MOCHS problem (i.e., MOCHS-100, MOCHS-200, MOCHS-300, and MOCHS-400 problems) was constructed in two steps. (1) The non-dominated solutions found by all the MOEAs over the 30 independent runs were captured; (2) From these solutions, we employed a clustering algorithm to select 6000 non-dominated solutions which define the reference set used by the IGD performance measure. In the case of the performance indicator, the reference vector r was obtained by finding the maximum value for each objective in the constructed reference Pareto front. On the other hand, the ideal point was stated by finding the minimum value for each objective in the reference Pareto front for the problem under consideration. In this way, the performance indicator shall consider, in a better measure, the extreme portions of the Pareto front approximation found by each MOEA.
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