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 35 articles , 1 standard article )

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  1. Akhtar, Taimoor; Shoemaker, Christine A.: Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection (2016)
  2. Martínez-Frutos, Jesús; Herrero-Pérez, David: Kriging-based infill sampling criterion for constraint handling in multi-objective optimization (2016)
  3. Rudolph, Günter; Schütze, Oliver; Grimme, Christian; Domínguez-Medina, Christian; Trautmann, Heike: Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results (2016)
  4. Schütze, Oliver; Martín, Adanay; Lara, Adriana; Alvarado, Sergio; Salinas, Eduardo; Coello Coello, Carlos A.: The directed search method for multi-objective memetic algorithms (2016)
  5. 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)
  6. Filomeno Coelho, Rajan: Bi-objective hypervolume-based Pareto optimization (2015)
  7. Li, Miqing; Yang, Shengxiang; Liu, Xiaohui: Bi-goal evolution for many-objective optimization problems (2015)
  8. Nguyen, Anh Quang; Sutton, Andrew M.; Neumann, Frank: Population size matters: rigorous runtime results for maximizing the hypervolume indicator (2015)
  9. Rubio-Largo, Álvaro; Vega-Rodríguez, Miguel A.; González-Álvarez, David L.: Multiobjective swarm intelligence for the traffic grooming problem (2015)
  10. Chen, Yu; Zou, Xiufen: Runtime analysis of a multi-objective evolutionary algorithm for obtaining finite approximations of Pareto fronts (2014)
  11. Couckuyt, Ivo; Deschrijver, Dirk; Dhaene, Tom: Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization (2014)
  12. Dai, Cai; Wang, Yuping; Ye, Miao: A new evolutionary algorithm based on contraction method for many-objective optimization problems (2014)
  13. Derbel, Bilel; Humeau, Jérémie; Liefooghe, Arnaud; Verel, Sébastien: Distributed localized bi-objective search (2014)
  14. Wang, Rui; Fleming, Peter J.; Purshouse, Robin C.: General framework for localised multi-objective evolutionary algorithms (2014)
  15. Bringmann, Karl; Friedrich, Tobias; Igel, Christian; Voß, Thomas: Speeding up many-objective optimization by Monte Carlo approximations (2013)
  16. Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.: Hypervolume-driven analytical programming for solar-powered irrigation system optimization (2013)
  17. Kim, Hyoungjin; Liou, Meng-Sing: New fitness sharing approach for multi-objective genetic algorithms (2013)
  18. Vaz, Daniel; Paquete, Luís; Ponte, Aníbal: A note on the $\epsilon$-indicator subset selection (2013)
  19. Wang, Weijia; Sebag, Michèle: Hypervolume indicator and dominance reward based multi-objective Monte-Carlo tree search (2013)
  20. Xu, Ying; Qu, Rong; Li, Renfa: A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems (2013)

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