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 94 articles , 1 standard article )

Showing results 1 to 20 of 94.
Sorted by year (citations)

1 2 3 4 5 next

  1. Paquete, Luís; Schulze, Britta; Stiglmayr, Michael; Lourenço, Ana C.: Computing representations using hypervolume scalarizations (2022)
  2. Audet, Charles; Bigeon, Jean; Cartier, Dominique; Le Digabel, Sébastien; Salomon, Ludovic: Performance indicators in multiobjective optimization (2021)
  3. Grimme, Christian; Kerschke, Pascal; Aspar, Pelin; Trautmann, Heike; Preuss, Mike; Deutz, André H.; Wang, Hao; Emmerich, Michael: Peeking beyond peaks: challenges and research potentials of continuous multimodal multi-objective optimization (2021)
  4. Hansen, Nikolaus; Auger, Anne; Ros, Raymond; Mersmann, Olaf; Tušar, Tea; Brockhoff, Dimo: COCO: a platform for comparing continuous optimizers in a black-box setting (2021)
  5. Kaur, Manjit; Singh, Dilbag: Multiobjective evolutionary optimization techniques based hyperchaotic map and their applications in image encryption (2021)
  6. Liagkouras, Konstantinos; Metaxiotis, Konstantinos: Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions (2021)
  7. Villarreal-Cervantes, Miguel Gabriel; Pantoja-García, Jesús Said; Rodríguez-Molina, Alejandro; Benitez-Garcia, Saul Enrique: Pareto optimal synthesis of eight-bar mechanism using meta-heuristic multi-objective search approaches: application to bipedal gait generation (2021)
  8. Wang, Hao; Sun, Chaoli; Zhang, Guochen; Fieldsend, Jonathan E.; Jin, Yaochu: Non-dominated sorting on performance indicators for evolutionary many-objective optimization (2021)
  9. Cocchi, Guido; Levato, Tommaso; Liuzzi, Giampaolo; Sciandrone, Marco: A concave optimization-based approach for sparse multiobjective programming (2020)
  10. Gaudrie, David; Le Riche, Rodolphe; Picheny, Victor; Enaux, Benoît; Herbert, Vincent: Targeting solutions in Bayesian multi-objective optimization: sequential and batch versions (2020)
  11. Guerreiro, Andreia P.; Fonseca, Carlos M.: An analysis of the hypervolume Sharpe-ratio indicator (2020)
  12. Hale, Joshua Q.; Zhu, Helin; Zhou, Enlu: Domination measure: a new metric for solving multiobjective optimization (2020)
  13. Liu, Songbai; Yu, Qiyuan; Lin, Qiuzhen; Tan, Kay Chen: An adaptive clustering-based evolutionary algorithm for many-objective optimization problems (2020)
  14. Li, Wenhua; Wang, Rui; Zhang, Tao; Ming, Mengjun; Li, Kaiwen: Reinvestigation of evolutionary many-objective optimization: focus on the Pareto knee front (2020)
  15. Luo, Jianping; Huang, Xiongwen; Yang, Yun; Li, Xia; Wang, Zhenkun; Feng, Jiqiang: A many-objective particle swarm optimizer based on indicator and direction vectors for many-objective optimization (2020)
  16. Raimundo, Marcos M.; Ferreira, Paulo A. V.; Von Zuben, Fernando J.: An extension of the non-inferior set estimation algorithm for many objectives (2020)
  17. Schütze, Oliver; Uribe, Lourdes; Lara, Adriana: The gradient subspace approximation and its application to bi-objective optimization problems (2020)
  18. Tang, Weisen; Liu, Hai-Lin; Chen, Lei; Tan, Kay Chen; Cheung, Yiu-ming: Fast hypervolume approximation scheme based on a segmentation strategy (2020)
  19. Ying, Weiqin; Huang, Junjie; Wu, Yu; Deng, Yali; Xie, Yuehong; Wang, Zhenyu; Lin, Zhiyi: Multi-dimensional tree guided efficient global association for decomposition-based evolutionary many-objective optimization (2020)
  20. Zhang, XuWei; Liu, Hao; Tu, LiangPing; Zhao, Jian: An efficient multi-objective optimization algorithm based on level swarm optimizer (2020)

1 2 3 4 5 next