MOCell: A cellular genetic algorithm for multiobjective optimization

By Antonio J. Nebro, Juan José Durillo, Francisco Luna, Bernabé Dorronsoro, Enrique Alba. International Journal of Intelligent Systems, 2009

309 citations recorded by the source corpus.

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. © 2009 Wiley Periodicals, Inc.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/fa02b932403871516fbf978549849650c14deda6

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Citations in the corpus

A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics Applications (2013, methodology): “For the three-objective problem, MOCeLL outperformed the other algorithms for all the performance measures adopted, except for the computational time (NSGA-II was the fastest technique). c) Duran et al. [57]: The authors of this paper compared the performance of three MOEAs and an SOEA, according to”
Distributed evolutionary algorithms and their models: A survey of the state-of-the-art (2015, background): “n comparison, the dMOEAs proposed in [93,34] employ cellular odels.”