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 343 articles )

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  1. Aalaei, Amin; Kayvanfar, Vahid; Davoudpour, Hamid: A multi-objective optimization for preemptive identical parallel machines scheduling problem (2017)
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  4. Iturriaga, Santiago; Dorronsoro, Bernabé; Nesmachnow, Sergio: Multiobjective evolutionary algorithms for energy and service level scheduling in a federation of distributed datacenters (2017)
  5. Kukkonen, Saku; Coello Coello, Carlos A.: Generalized differential evolution for numerical and evolutionary optimization (2017)
  6. 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)
  7. Redondo, J.L.; Fernández, J.; Ortigosa, P.M.: FEMOEA: a fast and efficient multi-objective evolutionary algorithm (2017)
  8. Salgueiro, Yamisleydi; Toro, Jorge L.; Bello, Rafael; Falcon, Rafael: Multiobjective variable mesh optimization (2017)
  9. Wang, Hao; Ren, Yiyi; Deutz, André; Emmerich, Michael: On steering dominated points in hypervolume indicator gradient ascent for bi-objective optimization (2017)
  10. Ye Tian, Ran Cheng, Xingyi Zhang, Yaochu Jin: PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization (2017) arXiv
  11. Yuxin, Zhao; Shenghong, Li; Feng, Jin: Overlapping community detection in complex networks using multi-objective evolutionary algorithm (2017)
  12. Zuo, Cili; Wu, Lianghong; Zeng, Zhao-Fu; Wei, Hua-Liang: Stochastic fractal based multiobjective fruit fly optimization (2017)
  13. Karasakal, Esra; Silav, Ahmet: A multi-objective genetic algorithm for a bi-objective facility location problem with partial coverage (2016)
  14. Lančinskas, Algirdas; Fernández, Pascual; Pelegrín, Blas; Žilinskas, Julius: Estimating the Pareto front of a hard bi-criterion competitive facility location problem (2016)
  15. Lei, Hongtao; Wang, Rui; Laporte, Gilbert: Solving a multi-objective dynamic stochastic districting and routing problem with a co-evolutionary algorithm (2016)
  16. Lin, Jianhua; Liu, Min; Hao, Jinghua; Jiang, Shenglong: A multi-objective optimization approach for integrated production planning under interval uncertainties in the steel industry (2016)
  17. Martí, Luis; García, Jesús; Berlanga, Antonio; Molina, José M.: MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm (2016)
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  19. Rohaninejad, Mohammad; Sahraeian, Rashed; Nouri, Behdin Vahedi: Multi-objective optimization of integrated lot-sizing and scheduling problem in flexible job shops (2016)
  20. Schlünz, E.B.; Bokov, P.M.; van Vuuren, J.H.: A comparative study on multiobjective metaheuristics for solving constrained in-core fuel management optimisation problems (2016)

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