Noise-induced features in robust multi-objective optimization problems
Abstract
Apart from the need to satisfy several competing objectives, many real-world applications are also sensitive to decision or environmental parameter variation which results in large or unacceptable performance variation. While evolutionary optimization techniques have several advantages over operational research methods for robust optimization, it is rarely studied by the evolutionary multi-objective (MO) optimization community. This paper addresses the issue of robust MO optimization by presenting a robust continuous MO test suite with features of noise-induced solution space, fitness landscape and decision space variation. The work presented in this paper should encourage further studies and the development of more effective algorithms for robust MO optimization.
Authors: Chi-Keong Goh, Kay Chen Tan, C. Y. Cheong, Yew-Soon Ong
Published in: IEEE Congress on Evolutionary Computation (CEC) (2007)