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 464 articles )

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  1. Cuevas, Erik; Becerra, Héctor; Luque, Alberto: Anisotropic diffusion filtering through multi-objective optimization (2021)
  2. Karsu, Özlem; Kara, Bahar Y.; Akkaya, Elif; Ozel, Aysu: Clean water network design for refugee camps (2021)
  3. Liagkouras, Konstantinos; Metaxiotis, Konstantinos: Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions (2021)
  4. Long, Qiang; Wu, Xue; Wu, Changzhi: Non-dominated sorting methods for multi-objective optimization: review and numerical comparison (2021)
  5. Zouache, Djaafar; Ben Abdelaziz, Fouad; Lefkir, Mira; Chalabi, Nour El-Houda: Guided moth-flame optimiser for multi-objective optimization problems (2021)
  6. Abouhawwash, Mohamed; Jameel, Mohammed; Deb, Kalyanmoy: A smooth proximity measure for optimality in multi-objective optimization using Benson’s method (2020)
  7. Alcaraz, Javier; Landete, Mercedes; Monge, Juan F.; Sainz-Pardo, José L.: Multi-objective evolutionary algorithms for a reliability location problem (2020)
  8. Alexandrino, Patricia da Silva Lopes; Gomes, Guilherme Ferreira; Cunha, Sebastião Simões jun.: A robust optimization for damage detection using multiobjective genetic algorithm, neural network and fuzzy decision making (2020)
  9. 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
  10. Bose, Amarnath: Using genetic algorithm to improve consistency and retain authenticity in the analytic hierarchy process (2020)
  11. Brandão, Martim; Jirotka, Marina; Webb, Helena; Luff, Paul: Fair navigation planning: a resource for characterizing and designing fairness in mobile robots (2020)
  12. Carvalho, Iago A.; Ribeiro, Marco A.: An exact approach for the minimum-cost bounded-error calibration tree problem (2020)
  13. Drake, John H.; Starkey, Andrew; Owusu, Gilbert; Burke, Edmund K.: Multiobjective evolutionary algorithms for strategic deployment of resources in operational units (2020)
  14. Hale, Joshua Q.; Zhu, Helin; Zhou, Enlu: Domination measure: a new metric for solving multiobjective optimization (2020)
  15. Han, Ding; Zheng, Jianrong: A Kriging model-based expensive multiobjective optimization algorithm using R2 indicator of expectation improvement (2020)
  16. 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)
  17. 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)
  18. Leng, Longlong; Zhang, Jingling; Zhang, Chunmiao; Zhao, Yanwei; Wang, Wanliang; Li, Gongfa: Decomposition-based hyperheuristic approaches for the bi-objective cold chain considering environmental effects (2020)
  19. Liang, Jing; Li, Zhimeng; Qu, Boyang; Yu, Kunjie; Qiao, Kangjia; Ge, Shilei: A knee point based NSGA-II multi-objective evolutionary algorithm (2020)
  20. Liang, Liang: A fusion multiobjective empire split algorithm (2020)

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