SMS-EMOA

SMS-EMOA: multiobjective selection based on dominated hypervolume. The hypervolume measure (or SS metric) is a frequently applied quality measure for comparing the results of evolutionary multiobjective optimisation algorithms (EMOA). The new idea is to aim explicitly for the maximisation of the dominated hypervolume within the optimisation process. A steady-state EMOA is proposed that features a selection operator based on the hypervolume measure combined with the concept of non-dominated sorting. The algorithm’s population evolves to a well-distributed set of solutions, thereby focussing on interesting regions of the Pareto front. The performance of the devised SSmetric selection EMOA (SMS-EMOA) is compared to state-of-the-art methods on two- and three-objective benchmark suites as well as on aeronautical real-world applications.


References in zbMATH (referenced in 74 articles , 1 standard article )

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  1. Cocchi, Guido; Levato, Tommaso; Liuzzi, Giampaolo; Sciandrone, Marco: A concave optimization-based approach for sparse multiobjective programming (2020)
  2. Gaudrie, David; Le Riche, Rodolphe; Picheny, Victor; Enaux, Benoît; Herbert, Vincent: Targeting solutions in Bayesian multi-objective optimization: sequential and batch versions (2020)
  3. Guerreiro, Andreia P.; Fonseca, Carlos M.: An analysis of the hypervolume Sharpe-ratio indicator (2020)
  4. Raimundo, Marcos M.; Ferreira, Paulo A. V.; Von Zuben, Fernando J.: An extension of the non-inferior set estimation algorithm for many objectives (2020)
  5. Schütze, Oliver; Uribe, Lourdes; Lara, Adriana: The gradient subspace approximation and its application to bi-objective optimization problems (2020)
  6. Zheng, Wei; Wu, Jianyu; Zhang, Chenghu; Sun, Jianyong: A clustering-based multiobjective evolutionary algorithm for balancing exploration and exploitation (2020)
  7. Bai, Hui; Zheng, Jinhua; Yu, Guo; Yang, Shengxiang; Zou, Juan: A Pareto-based many-objective evolutionary algorithm using space partitioning selection and angle-based truncation (2019)
  8. Bao, Chunteng; Xu, Lihong; Goodman, Erik D.: A novel two-archive matching-based algorithm for multi- and many-objective optimization (2019)
  9. Khan, Burhan; Hanoun, Samer; Johnstone, Michael; Lim, Chee Peng; Creighton, Douglas; Nahavandi, Saeid: A scalarization-based dominance evolutionary algorithm for many-objective optimization (2019)
  10. Lin, Wu; Lin, Qiuzhen; Zhu, Zexuan; Li, Jianqiang; Chen, Jianyong; Ming, Zhong: Evolutionary search with multiple utopian reference points in decomposition-based multiobjective optimization (2019)
  11. Santiago, Alejandro; Dorronsoro, Bernabé; Nebro, Antonio J.; Durillo, Juan J.; Castillo, Oscar; Fraire, Héctor J.: A novel multi-objective evolutionary algorithm with fuzzy logic based adaptive selection of operators: FAME (2019)
  12. Yang, Kaifeng; Emmerich, Michael; Deutz, André; Bäck, Thomas: Efficient computation of expected hypervolume improvement using box decomposition algorithms (2019)
  13. Zhang, Hu; Sun, Jianyong; Liu, Tonglin; Zhang, Ke; Zhang, Qingfu: Balancing exploration and exploitation in multiobjective evolutionary optimization (2019)
  14. Zhou, Yuren; He, Xiaoyu; Xiang, Yi; Cai, Shaowei: A set of new multi- and many-objective test problems for continuous optimization and a comprehensive experimental evaluation (2019)
  15. Zou, Juan; Fu, Liuwei; Yang, Shengxiang; Zheng, Jinhua; Ruan, Gan; Pei, Tingrui; Wang, Lei: An adaptation reference-point-based multiobjective evolutionary algorithm (2019)
  16. Al-Dujaili, Abdullah; Suresh, S.: Multi-objective simultaneous optimistic optimization (2018)
  17. Gomes, Ricardo J.; Guerreiro, Andreia P.; Kuhn, Tobias; Paquete, Luís: Implicit enumeration strategies for the hypervolume subset selection problem (2018)
  18. Wong, C. S. Y.; Al-Dujaili, Abdullah; Suresh, S.; Sundararajan, N.: Pareto-aware strategies for faster convergence in multi-objective multi-scale search optimization (2018)
  19. Greiner, David; Periaux, Jacques; Emperador, Jose M.; Galván, Blas; Winter, Gabriel: Game theory based evolutionary algorithms: a review with Nash applications in structural engineering optimization problems (2017)
  20. Redondo, J. L.; Fernández, J.; Ortigosa, P. M.: FEMOEA: a fast and efficient multi-objective evolutionary algorithm (2017)

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