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 60 articles , 1 standard article )

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  1. Gaudrie, David; Le Riche, Rodolphe; Picheny, Victor; Enaux, Benoît; Herbert, Vincent: Targeting solutions in Bayesian multi-objective optimization: sequential and batch versions (2020)
  2. Guerreiro, Andreia P.; Fonseca, Carlos M.: An analysis of the hypervolume Sharpe-ratio indicator (2020)
  3. Raimundo, Marcos M.; Ferreira, Paulo A. V.; Von Zuben, Fernando J.: An extension of the non-inferior set estimation algorithm for many objectives (2020)
  4. 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)
  5. Yang, Kaifeng; Emmerich, Michael; Deutz, André; Bäck, Thomas: Efficient computation of expected hypervolume improvement using box decomposition algorithms (2019)
  6. 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)
  7. Gomes, Ricardo J.; Guerreiro, Andreia P.; Kuhn, Tobias; Paquete, Luís: Implicit enumeration strategies for the hypervolume subset selection problem (2018)
  8. 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)
  9. Redondo, J. L.; Fernández, J.; Ortigosa, P. M.: FEMOEA: a fast and efficient multi-objective evolutionary algorithm (2017)
  10. Singh, Prashant; Couckuyt, Ivo; Elsayed, Khairy; Deschrijver, Dirk; Dhaene, Tom: Multi-objective geometry optimization of a gas cyclone using triple-fidelity co-Kriging surrogate models (2017)
  11. Wang, Hao; Ren, Yiyi; Deutz, André; Emmerich, Michael: On steering dominated points in hypervolume indicator gradient ascent for bi-objective optimization (2017)
  12. Akhtar, Taimoor; Shoemaker, Christine A.: Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection (2016)
  13. Martínez-Frutos, Jesús; Herrero-Pérez, David: Kriging-based infill sampling criterion for constraint handling in multi-objective optimization (2016)
  14. Rudolph, Günter; Schütze, Oliver; Grimme, Christian; Domínguez-Medina, Christian; Trautmann, Heike: Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results (2016)
  15. Schütze, Oliver; Martín, Adanay; Lara, Adriana; Alvarado, Sergio; Salinas, Eduardo; Coello Coello, Carlos A.: The directed search method for multi-objective memetic algorithms (2016)
  16. Allmendinger, Richard; Handl, Julia; Knowles, Joshua: Multiobjective optimization: when objectives exhibit non-uniform latencies (2015)
  17. Cao, Yongtao; Smucker, Byran J.; Robinson, Timothy J.: On using the hypervolume indicator to compare Pareto fronts: applications to multi-criteria optimal experimental design (2015)
  18. Dubois-Lacoste, Jérémie; López-Ibáñez, Manuel; Stützle, Thomas: Anytime Pareto local search (2015)
  19. Filomeno Coelho, Rajan: Bi-objective hypervolume-based Pareto optimization (2015)
  20. Li, Ke; Kwong, Sam; Deb, Kalyanmoy: A dual-population paradigm for evolutionary multiobjective optimization (2015)

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