@inproceedings{c50d0e06917d4f04a2f341e13264bb02,
title = "A novel algorithm for optimizing the Pareto set in dynamic problem spaces",
abstract = "This paper presents an algorithm based on dynamic multiobjective optimization (DMO) which employs a single randomly mutating time-variant archive to balance convergence and diversity in order to efficiently select the final, non-dominated Pareto set. The algorithm is tested on selected dynamic optimization benchmark functions, and the improvement in the performance of the single archive approach is validated by the improved performance metrics and overall computational time. Overall, the proposed single-Archive algorithm (called DOAEA) generated better metrics and faster computational time for the Gee-Tan-Abbas (GTA) test suite for average MIGD and average MHV compared to previously proposed two-Archive algorithm, DTAEA.",
keywords = "Pareto optimality, Pareto set, convergence, diversity, dynamic multiobjective optimization",
author = "Essiet, {Ima O.} and Yanxia Sun and Zenghui Wang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 Conference on Information Communications Technology and Society, ICTAS 2018 ; Conference date: 08-03-2018 Through 09-03-2018",
year = "2018",
month = may,
day = "29",
doi = "10.1109/ICTAS.2018.8368762",
language = "English",
series = "2018 Conference on Information Communications Technology and Society, ICTAS 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
booktitle = "2018 Conference on Information Communications Technology and Society, ICTAS 2018 - Proceedings",
address = "United States",
}