SPEA2

SPEA2 - The Strength Pareto Evolutionary Algorithm 2: SPEA2 in an elitist multiobjective evolutionary algorithm. It is an improved version of the Strength Pareto EA (SPEA) and incorporates a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method. SPEA2 operates with a population (archive) of fixed size, from which promising candidated are drawn as parents of the next generation. The resulting offspring then compete with the old ones for inclusion in the population


References in zbMATH (referenced in 454 articles )

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  1. Cuevas, Erik; Becerra, Héctor; Luque, Alberto: Anisotropic diffusion filtering through multi-objective optimization (2021)
  2. Abouhawwash, Mohamed; Jameel, Mohammed; Deb, Kalyanmoy: A smooth proximity measure for optimality in multi-objective optimization using Benson’s method (2020)
  3. Alcaraz, Javier; Landete, Mercedes; Monge, Juan F.; Sainz-Pardo, José L.: Multi-objective evolutionary algorithms for a reliability location problem (2020)
  4. Aravind Krishnamoorthy, Ankit Mishra, Deepak Kamal, Sungwook Hong, Ken-ichi Nomura, Subodh Tiwari, Aiichiro Nakano, Rajiv Kalia, Rampi Ramprasad, Priya Vashishta: EZFF: Python Library for Multi-Objective Parameterization and Uncertainty Quantification of Interatomic Forcefields for Molecular Dynamics (2020) arXiv
  5. Bose, Amarnath: Using genetic algorithm to improve consistency and retain authenticity in the analytic hierarchy process (2020)
  6. Brandão, Martim; Jirotka, Marina; Webb, Helena; Luff, Paul: Fair navigation planning: a resource for characterizing and designing fairness in mobile robots (2020)
  7. Carvalho, Iago A.; Ribeiro, Marco A.: An exact approach for the minimum-cost bounded-error calibration tree problem (2020)
  8. Drake, John H.; Starkey, Andrew; Owusu, Gilbert; Burke, Edmund K.: Multiobjective evolutionary algorithms for strategic deployment of resources in operational units (2020)
  9. Hale, Joshua Q.; Zhu, Helin; Zhou, Enlu: Domination measure: a new metric for solving multiobjective optimization (2020)
  10. Hasani, Ali; Hosseini, Seyed Mohammad Hassan: A bi-objective flexible flow shop scheduling problem with machine-dependent processing stages: trade-off between production costs and energy consumption (2020)
  11. Jiang, Shouyong; Li, Hongru; Guo, Jinglei; Zhong, Mingjun; Yang, Shengxiang; Kaiser, Marcus; Krasnogor, Natalio: AREA: an adaptive reference-set based evolutionary algorithm for multiobjective optimisation (2020)
  12. Liang, Jing; Li, Zhimeng; Qu, Boyang; Yu, Kunjie; Qiao, Kangjia; Ge, Shilei: A knee point based NSGA-II multi-objective evolutionary algorithm (2020)
  13. Liang, Liang: A fusion multiobjective empire split algorithm (2020)
  14. Liu, Ruochen; Zhou, Runan; Ren, Rui; Liu, Jiangdi; Jiao, Licheng: Multi-layer interaction preference based multi-objective evolutionary algorithm through decomposition (2020)
  15. Liu, Yuan; Zhu, Ningbo; Li, Kenli; Li, Miqing; Zheng, Jinhua; Li, Keqin: An angle dominance criterion for evolutionary many-objective optimization (2020)
  16. Liu, Zhi-Zhong; Wang, Yong; Huang, Pei-Qiu: AnD: a many-objective evolutionary algorithm with angle-based selection and shift-based density estimation (2020)
  17. Ma, Lianbo; Wang, Rui; Chen, Shengminjie; Cheng, Shi; Wang, Xingwei; Lin, Zhiwei; Shi, Yuhui; Huang, Min: A novel many-objective evolutionary algorithm based on transfer matrix with kriging model (2020)
  18. Raimundo, Marcos M.; Ferreira, Paulo A. V.; Von Zuben, Fernando J.: An extension of the non-inferior set estimation algorithm for many objectives (2020)
  19. Vo-Duy, T.; Duong-Gia, D.; Ho-Huu, V.; Nguyen-Thoi, T.: An effective couple method for reliability-based multi-objective optimization of truss structures with static and dynamic constraints (2020)
  20. Zhang, Weiwei; Zhang, Ningjun; Wan, Hanwen; Huang, Daoying; Wen, Xiaoyu; Meng, Yinghui: Decomposition based differentiate evolution algorithm with niching strategy for multimodal multi-objective optimization (2020)

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Further publications can be found at: http://www.tik.ee.ethz.ch/pisa/?page=bugs.php