MOCell

MOCell: A cellular genetic algorithm for multiobjective optimization. This paper introduces a new cellular genetic algorithm for solving multiobjective continuous optimization problems. Our approach is characterized by using an external archive to store nondominated solutions and a feedback mechanism in which solutions from this archive randomly replace existing individuals in the population after each iteration. The result is a simple and elitist algorithm called MOCell. Our proposal has been evaluated with both constrained and unconstrained problems and compared against NSGA-II and SPEA2, two state-of-the-art evolutionary multiobjective optimizers. For the studied benchmark, our experiments indicate that MOCell obtains competitive results in terms of convergence and hypervolume, and it clearly outperforms the other two compared algorithms concerning the diversity of the solutions along the Pareto front.


References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. L. Lobo, Jesus; Del Ser, Javier; Herrera, Francisco: LUNAR: cellular automata for drifting data streams (2021)
  2. 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)
  3. Iturriaga, Santiago; Dorronsoro, Bernabé; Nesmachnow, Sergio: Multiobjective evolutionary algorithms for energy and service level scheduling in a federation of distributed datacenters (2017)
  4. Salgueiro, Yamisleydi; Toro, Jorge L.; Bello, Rafael; Falcon, Rafael: Multiobjective variable mesh optimization (2017)
  5. Chaves-González, José M.; Pérez-Toledano, Miguel A.: Differential evolution with Pareto tournament for the multi-objective next release problem (2015)
  6. Alba, Enrique; Luque, Gabriel; Nesmachnow, Sergio: Parallel metaheuristics: recent advances and new trends (2013)
  7. Dorronsoro, Bernabé; Danoy, Grégoire; Nebro, Antonio J.; Bouvry, Pascal: Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution (2013)
  8. Nesmachnow, Sergio: Parallel multiobjective evolutionary algorithms for batch scheduling in heterogeneous computing and grid systems (2013)
  9. Zhang, Yi; Zhang, Hu; Lu, Chao: Study on parameter optimization design of drum brake based on hybrid cellular multiobjective genetic algorithm (2012)
  10. Nebro, Antonio J.; Durillo, Juan J.; Luna, Francisco; Dorronsoro, Bernabé; Alba, Enrique: MOCell: A cellular genetic algorithm for multiobjective optimization (2009)