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 357 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. 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)
  3. Zheng, Y.; Chen, J.: A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser (2018)
  4. Zouache, Djaafar; Moussaoui, Abdelouahab; Ben Abdelaziz, Fouad: A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the Knapsack problem (2018)
  5. Aalaei, Amin; Kayvanfar, Vahid; Davoudpour, Hamid: A multi-objective optimization for preemptive identical parallel machines scheduling problem (2017)
  6. 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)
  7. Dolgui, A. B.; Eremeev, A. V.; Sigaev, V. S.: Analysis of a multicriterial buffer capacity optimization problem for a production line (2017)
  8. Feliot, Paul; Bect, Julien; Vazquez, Emmanuel: A Bayesian approach to constrained single- and multi-objective optimization (2017)
  9. Filatovas, Ernestas; Lančinskas, Algirdas; Kurasova, Olga; Žilinskas, Julius: A preference-based multi-objective evolutionary algorithm R-NSGA-II with stochastic local search (2017)
  10. Iturriaga, Santiago; Dorronsoro, Bernabé; Nesmachnow, Sergio: Multiobjective evolutionary algorithms for energy and service level scheduling in a federation of distributed datacenters (2017)
  11. Kerkhove, L.-P.; Vanhoucke, M.: A parallel multi-objective scatter search for optimising incentive contract design in projects (2017)
  12. Kukkonen, Saku; Coello Coello, Carlos A.: Generalized differential evolution for numerical and evolutionary optimization (2017)
  13. Liu, Ruochen; Li, Jianxia; Fan, Jing; Mu, Caihong; Jiao, Licheng: A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization (2017)
  14. Lwin, Khin T.; Qu, Rong; MacCarthy, Bart L.: Mean-VaR portfolio optimization: a nonparametric approach (2017)
  15. Ortega, Gloria; Filatovas, Ernestas; Garzón, E. M.; Casado, L. G.: Non-dominated sorting procedure for Pareto dominance ranking on multicore CPU and/or GPU (2017)
  16. Redondo, J. L.; Fernández, J.; Ortigosa, P. M.: FEMOEA: a fast and efficient multi-objective evolutionary algorithm (2017)
  17. Salgueiro, Yamisleydi; Toro, Jorge L.; Bello, Rafael; Falcon, Rafael: Multiobjective variable mesh optimization (2017)
  18. Wang, Hao; Ren, Yiyi; Deutz, André; Emmerich, Michael: On steering dominated points in hypervolume indicator gradient ascent for bi-objective optimization (2017)
  19. Wang, Honggang: Multi-objective retrospective optimization using stochastic zigzag search (2017)
  20. Ye Tian, Ran Cheng, Xingyi Zhang, Yaochu Jin: PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization (2017) arXiv

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