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A fuzzy logic controller applied to a diversity-based multi-objective evolutionary algorithm for single-objective optimisation

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Abstract

In recent years, Multi-Objective Evolutionary Algorithms (moeas) that consider diversity as an objective have been used to tackle single-objective optimisation problems. The ability to deal with premature convergence has been greatly improved with these schemes. However, they usually increase the number of free parameters that need to be tuned. To improve results and avoid the tedious hand-tuning of algorithms, the use of automated parameter control approaches that are able to adapt parameter values during the course of an evolutionary run are becoming more common in the field of Evolutionary Computation (ec). This research focuses on the application of parameter control approaches to diversity-based moeas. Two external parameter control methods are investigated; a novel method based on Fuzzy Logic and a recently proposed Hyper-heuristic. These are compared to an internal control method that uses self-adaptation. An extensive comparison of the three methods is carried out using a set of single-objective benchmark problems of diverse complexity. Analyses include comparisons to a wide range of schemes with fixed parameters and to a single-objective approach. The results show that the fuzzy logic and hyper-heuristic methods are able to find similar or better solutions than the fixed parameter methods for a significant number of problems, with considerable savings in computational resources and time, whereas the self-adaptive strategy provides little benefit. Finally, we also demonstrate that the controlled diversity-based moea  outperforms the single-objective scheme in most cases, thus showing the benefits of solving single-objective problems through diversity-based multi-objective schemes.

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Notes

  1. Although a recent publication attempts to address this with a hyper-heuristic that is able to adapt the parameters of the low-level heuristics (Ren et al. 2012).

  2. Only the fuzzy logic operator and is used in the antecedents of the fuzzy rules.

  3. The complete specifications for all of the rule bases designed for both versions of the flc are available as online supplementary material.

  4. Due to space constraints, the graphics for every problem are not shown but are available as online supplementary material.

    Fig. 3
    figure 3

    Mean objective value achieved by the parameter control methods and by the diversity-based moea executed with fixed values of the parameter th

    Full size image

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Acknowledgments

This work was supported by the ec (feder) and the Spanish Ministry of Science and Innovation as part of the ‘Plan Nacional de i+d+i’, with contract number tin2011-25448. The work of Eduardo Segredo was funded by grant fpu-ap2009-0457. The work was also funded by the hpc-europa2 project (project number: 228398) with the support of the European Commission—Capacities Area—Research Infrastructures. The second author also acknowledges the financial support from CONCYTEG as part of the plan ’Investigadores Jóvenes - DPP-2014’.

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Authors and Affiliations

  1. Dpto. Ingeniería Informática y de Sistemas, Universidad de La Laguna, Avda. Astrofísico Fco. Sánchez. Edif. Matemáticas, 38071, Santa Cruz de Tenerife, Spain

    Eduardo Segredo & Coromoto León

  2. Área Computación, Centro de Investigación en Matemáticas, Callejón Jalisco s/n, Mineral de Valenciana, 36240 , Guanajuato, Guanajuato, Mexico

    Carlos Segura

  3. Institute for Informatics and Digital Innovation, Edinburgh Napier University, Edinburgh, Scotland, UK

    Emma Hart

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  1. Eduardo Segredo
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  2. Carlos Segura
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  3. Coromoto León
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  4. Emma Hart
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Correspondence to Eduardo Segredo.

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Communicated by V. Loia.

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Segredo, E., Segura, C., León, C. et al. A fuzzy logic controller applied to a diversity-based multi-objective evolutionary algorithm for single-objective optimisation. Soft Comput 19, 2927–2945 (2015). https://doi.org/10.1007/s00500-014-1454-y

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