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 404 articles )

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  1. Babazadeh, Hossein; Esfahanipour, Akbar: A novel multi period mean-VaR portfolio optimization model considering practical constraints and transaction cost (2019)
  2. Chen, Chen; Wei, Yu: Robust multiobjective portfolio optimization: a set order relations approach (2019)
  3. 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)
  4. 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)
  5. 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)
  6. 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)
  7. Palomo-Martínez, Pamela J.; Salazar-Aguilar, M. Angélica: The bi-objective traveling purchaser problem with deliveries (2019)
  8. 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)
  9. Askar, S. S.; Abouhawwash, M.: Quantity and price competition in a differentiated triopoly: static and dynamic investigations (2018)
  10. 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)
  11. Clempner, Julio B.; Poznyak, Alexander S.: Constructing the Pareto front for multi-objective Markov chains handling a strong Pareto policy approach (2018)
  12. Jafarzadeh, Hassan; Fleming, Cody H.: An exact geometry-based algorithm for path planning (2018)
  13. Leung, Chris S. K.; Lau, Henry Y. K.: Multiobjective simulation-based optimization based on artificial immune systems for a distribution center (2018)
  14. Luo, Naili; Li, Xia; Lin, Qiuzhen: Objective reduction for many-objective optimization problems using objective subspace extraction (2018)
  15. Moreno, Juan J.; Ortega, Gloria; Filatovas, Ernestas; Martínez, J. A.; Garzón, E. M.: Improving the performance and energy of non-dominated sorting for evolutionary multiobjective optimization on GPU/CPU platforms (2018)
  16. Nouri, Nouha; Ladhari, Talel: Evolutionary multiobjective optimization for the multi-machine flow shop scheduling problem under blocking (2018)
  17. Ortega-Casanova, J.; Lai, C.-H.: CFD study on laminar mixing at a very low Reynolds number by pitching and heaving a square cylinder (2018)
  18. Zheng, Yaochen; Chen, Jianqiao: A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser (2018)
  19. Zouache, Djaafar; Moussaoui, Abdelouahab; Ben Abdelaziz, Fouad: A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the Knapsack problem (2018)
  20. Aalaei, Amin; Kayvanfar, Vahid; Davoudpour, Hamid: A multi-objective optimization for preemptive identical parallel machines scheduling problem (2017)

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