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Multiobjective evolutionary algorithm based on decomposition for 3-objective optimization problems with objectives in different scales


In Multiobjective Optimization problems the objective functions may have different scales, which leads to a neglecting of one or more objective functions. The most common-used way in the literature to solve this drawback is to normalize the objective space; however, a set of uniformly distributed solutions in the normalized objective space may not be uniformly distributed in the original objective space with more than two objective functions. In this work, we present an improved version of the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) which incorporates a new aggregation technique based on the Normal Boundary Intersection approach and the Tchebycheff approach (MOEA/D-NBI) for solving 3-objective optimization problems with different scales of objectives.

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This work was partially funded by the Spanish Ministry of Economy and Competitiveness and the ERDF (European Regional Development Fund), under the contract TIN2012-30685 (BIO project). A. Rubio-Largo is supported by the postdoc research grant ACCION-III-13 from the University of Extremadura.

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Correspondence to Álvaro Rubio-Largo.

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Communicated by A. Castiglione.

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Rubio-Largo, Á., Zhang, Q. & Vega-Rodríguez, M.A. Multiobjective evolutionary algorithm based on decomposition for 3-objective optimization problems with objectives in different scales. Soft Comput 19, 157–166 (2015).

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  • Multiobjective optimization
  • Evolutionary algorithms
  • Pareto optimally
  • Normal Boundary Intersection