@inproceedings{2006008,
abstract = {Most real-world optimization problems involve objectives, constraints, and parameters which constantly change with time. Treating such problems as a stationary optimization problem demand the knowledge of the pattern of change a priori and even then the procedure can be computationally expensive. Although dynamic consideration using evolutionary algorithms has been made for single-objective optimization problems, there has been a lukewarm interest in formulating and solving dynamic multi-objective optimization problems. In this paper, we modify the commonly-used NSGA-II procedure in tracking a new Pareto-optimal front, as soon as there is a change in the problem. Introduction of a few random solutions or a few mutated solutions are investigated in detail. The approaches are tested and compared on a test problem and a real-world optimization of a hydro-thermal power scheduling problem. This systematic study is able to find a minimum frequency of change allowed in a problem for two dynamic EMO procedures to adequately track Pareto-optimal frontiers on-line. Based on these results, this paper also suggests an automatic decision-making procedure for arriving at a dynamic single optimal solution on-line. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.},
author = {Deb, Kalyanmoy and {Rao N.}, Udaya Bhaskara and Karthik, S.},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-540-70928-2_60},
isbn = {9783540709275},
issn = {16113349},
pages = {803--817},
publisher = {Springer, Berlin, Heidelberg},
title = {{Dynamic multi-objective optimization and decision-making using modified NSGA-II: A case study on hydro-thermal power scheduling}},
url = {https://link.springer.com/chapter/10.1007/978-3-540-70928-2{\_}60},
volume = {4403 LNCS},
year = {2007}
}