Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-540-49774-5_20
Title: Evolving the tradeoffs between Pareto-optimality and robustness in multi-objective evolutionary algorithms
Authors: Goh, C.K.
Tan, K.C. 
Issue Date: 2007
Source: Goh, C.K.,Tan, K.C. (2007). Evolving the tradeoffs between Pareto-optimality and robustness in multi-objective evolutionary algorithms. Studies in Computational Intelligence 51 : 457-478. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-540-49774-5_20
Abstract: Many real-world applications involve the simultaneous optimization of several competing objectives and are susceptible to decision or environmental parameter variation which results in large or unacceptable performance variation. While several studies on robust optimization have been presented in the domain of singleobjective (SO) problems, the evolution of robust solutions is rarely studied in the context of evolutionary multi-objective optimization (EMOO). This chapter presents a robust multi-objective evolutionary algorithm for constrained multi-objective optimization. The proposed algorithm, incorporating the features of micro-GA which performs a local search for the worst case scenario of each candidate solution, the memory-based feature of tabu restriction to guide the evolutionary process and periodic re-evaluation of archived solutions to reduce uncertainty of evolved solutions, is capable of evolving the tradeoffs between Pareto optimality and robustness. The effectiveness of the algorithm is validated upon two benchmark with different properties and the I-beam design problem. © Springer-Verlag Berlin Heidelberg 2007.
Source Title: Studies in Computational Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/55940
ISBN: 3540497722
ISSN: 1860949X
DOI: 10.1007/978-3-540-49774-5_20
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