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 368 articles )

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  1. Askar, S. S.; Abouhawwash, M.: Quantity and price competition in a differentiated triopoly: static and dynamic investigations (2018)
  2. Clempner, Julio B.; Poznyak, Alexander S.: Constructing the Pareto front for multi-objective Markov chains handling a strong Pareto policy approach (2018)
  3. Jafarzadeh, Hassan; Fleming, Cody H.: An exact geometry-based algorithm for path planning (2018)
  4. Leung, Chris S. K.; Lau, Henry Y. K.: Multiobjective simulation-based optimization based on artificial immune systems for a distribution center (2018)
  5. Luo, Naili; Li, Xia; Lin, Qiuzhen: Objective reduction for many-objective optimization problems using objective subspace extraction (2018)
  6. 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)
  7. Nouri, Nouha; Ladhari, Talel: Evolutionary multiobjective optimization for the multi-machine flow shop scheduling problem under blocking (2018)
  8. 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)
  9. Zheng, Y.; Chen, J.: A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser (2018)
  10. Zouache, Djaafar; Moussaoui, Abdelouahab; Ben Abdelaziz, Fouad: A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the Knapsack problem (2018)
  11. Aalaei, Amin; Kayvanfar, Vahid; Davoudpour, Hamid: A multi-objective optimization for preemptive identical parallel machines scheduling problem (2017)
  12. Abouhawwash, Mohamed; Seada, Haitham; Deb, Kalyanmoy: Towards faster convergence of evolutionary multi-criterion optimization algorithms using Karush Kuhn Tucker optimality based local search (2017)
  13. Capitanescu, F.; Marvuglia, A.; Benetto, E.; Ahmadi, A.; Tiruta-Barna, L.: Linear programming-based directed local search for expensive multi-objective optimization problems: application to drinking water production plants (2017)
  14. Dolgui, A. B.; Eremeev, A. V.; Sigaev, V. S.: Analysis of a multicriterial buffer capacity optimization problem for a production line (2017)
  15. Feliot, Paul; Bect, Julien; Vazquez, Emmanuel: A Bayesian approach to constrained single- and multi-objective optimization (2017)
  16. Filatovas, Ernestas; Lančinskas, Algirdas; Kurasova, Olga; Žilinskas, Julius: A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search (2017)
  17. Greiner, David; Periaux, Jacques; Emperador, Jose M.; Galván, Blas; Winter, Gabriel: Game theory based evolutionary algorithms: a review with Nash applications in structural engineering optimization problems (2017)
  18. Halassi, Abdoul-hafar: An attractor-based multiobjective particle swarm optimization (2017)
  19. Iturriaga, Santiago; Dorronsoro, Bernabé; Nesmachnow, Sergio: Multiobjective evolutionary algorithms for energy and service level scheduling in a federation of distributed datacenters (2017)
  20. Jia, Chunhua; Zhu, Hong: An improved multiobjective particle swarm optimization based on culture algorithms (2017)

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