@inproceedings{
 title = {Integrating user preferences and decomposition methods for many-objective optimization},
 type = {inproceedings},
 year = {2014},
 pages = {421-428},
 month = {9},
 publisher = {Institute of Electrical and Electronics Engineers Inc.},
 day = {16},
 id = {87f470d6-5f2d-30bc-96f0-349906d59f6e},
 created = {2024-03-31T02:37:58.736Z},
 accessed = {2021-02-28},
 file_attached = {false},
 profile_id = {cc2743c9-5fd7-3bb8-82e6-88c4edec0708},
 group_id = {e1abe20f-4b77-35da-9453-cb1a3f477d22},
 last_modified = {2024-03-31T02:37:58.736Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 citation_key = {Mohammadi2014},
 private_publication = {false},
 abstract = {Evolutionary algorithms that rely on dominance ranking often suffer from a low selection pressure problem when dealing with many-objective problems. Decomposition and user-preference based methods can help to alleviate this problem to a great extent. In this paper, a user-preference based evolutionary multi-objective algorithm is proposed that uses decomposition methods for solving many-objective problems. Decomposition techniques that are widely used in multi-objective evolutionary optimization require a set of evenly distributed weight vectors to generate a diverse set of solutions on the Pareto-optimal front. The newly proposed algorithm, R-MEAD2, improves the scalability of its previous version, R-MEAD, which uses a simplexlattice design method for generating weight vectors. This makes the population size is dependent on the dimension size of the objective space. R-MEAD2 uses a uniform random number generator to remove the coupling between dimension and the population size. This paper shows that a uniform random number generator is simple and able to generate evenly distributed points in a high dimensional space. Our comparative study shows that R-MEAD2 outperforms the dominance-based method R-NSGA-II on many-objective problems.},
 bibtype = {inproceedings},
 author = {Mohammadi, Asad and Omidvar, Mohammad Nabi and Li, Xiaodong and Deb, Kalyanmoy},
 doi = {10.1109/CEC.2014.6900595},
 booktitle = {Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014}
}

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