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 417 articles )

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  1. Abouhawwash, Mohamed; Jameel, Mohammed; Deb, Kalyanmoy: A smooth proximity measure for optimality in multi-objective optimization using Benson’s method (2020)
  2. Alcaraz, Javier; Landete, Mercedes; Monge, Juan F.; Sainz-Pardo, José L.: Multi-objective evolutionary algorithms for a reliability location problem (2020)
  3. Drake, John H.; Starkey, Andrew; Owusu, Gilbert; Burke, Edmund K.: Multiobjective evolutionary algorithms for strategic deployment of resources in operational units (2020)
  4. Raimundo, Marcos M.; Ferreira, Paulo A. V.; Von Zuben, Fernando J.: An extension of the non-inferior set estimation algorithm for many objectives (2020)
  5. Babazadeh, Hossein; Esfahanipour, Akbar: A novel multi period mean-VaR portfolio optimization model considering practical constraints and transaction cost (2019)
  6. Chen, Chen; Wei, Yu: Robust multiobjective portfolio optimization: a set order relations approach (2019)
  7. Gutierrez, Juan Carlos Ticona; Adamatti, Daniela Santini; Bravo, Juan Martin: A new stopping criterion for multi-objective evolutionary algorithms: application in the calibration of a hydrologic model (2019)
  8. Kar, Mohuya B.; Kar, Samarjit; Guo, Sini; Li, Xiang; Majumder, Saibal: A new bi-objective fuzzy portfolio selection model and its solution through evolutionary algorithms (2019)
  9. Li, Hao-ran; He, Fa-zhi; Yan, Xiao-hu: IBEA-SVM: an indicator-based evolutionary algorithm based on pre-selection with classification guided by SVM (2019)
  10. 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)
  11. Palomo-Martínez, Pamela J.; Salazar-Aguilar, M. Angélica: The bi-objective traveling purchaser problem with deliveries (2019)
  12. Wang, Yechuang; Cui, Zhihua; Li, Wuchao: A novel coupling algorithm based on glowworm swarm optimization and bacterial foraging algorithm for solving multi-objective optimization problems (2019)
  13. 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)
  14. Askar, S. S.; Abouhawwash, M.: Quantity and price competition in a differentiated triopoly: static and dynamic investigations (2018)
  15. Barbosa, Paulo Alberto Melo; Pinheiro, Plácido Rogério; de Vasconcelos Silveira, Francisca Raquel: Towards the verbal decision analysis paradigm for implementable prioritization of software requirements (2018)
  16. Clempner, Julio B.; Poznyak, Alexander S.: Constructing the Pareto front for multi-objective Markov chains handling a strong Pareto policy approach (2018)
  17. Jafarzadeh, Hassan; Fleming, Cody H.: An exact geometry-based algorithm for path planning (2018)
  18. Leung, Chris S. K.; Lau, Henry Y. K.: Multiobjective simulation-based optimization based on artificial immune systems for a distribution center (2018)
  19. Luo, Jungang; Sun, Xiaomei; Qi, Yutao; Xie, Jiancang: Approximating the irregularly shaped Pareto front of multi-objective reservoir flood control operation problem (2018)
  20. Luo, Naili; Li, Xia; Lin, Qiuzhen: Objective reduction for many-objective optimization problems using objective subspace extraction (2018)

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