Soft Computing

, Volume 22, Issue 2, pp 541–570 | Cite as

Dynamic differential evolution with combined variants and a repair method to solve dynamic constrained optimization problems: an empirical study

  • María-Yaneli Ameca-Alducin
  • Efrén Mezura-Montes
  • Nicandro Cruz-Ramírez
Methodologies and Application
  • 264 Downloads

Abstract

An empirical study of the algorithm dynamic differential evolution with combined variants with a repair method (DDECV \(+\) Repair) in the solution of dynamic constrained optimization problems is presented. Unexplored aspects of the algorithm are of particular interest in this work: (1) the role of each one of its elements, (2) its sensitivity to different change frequencies and change severities in the objective function and the constraints, (3) its ability to detect a change and recover after it, besides its diversity handling (percentage of feasible and infeasible solutions) during the search, and (4) its performance with dynamism present in different parts of the problem. Seven performance measures, eighteen recently proposed test problems and eight algorithms found in the specialized literature are considered in four experiments. The statistically validated results indicate that DDECV \(+\) Repair is robust to change frequency and severity variations, and that it is particularly fast to recover after a change in the environment, but highly depends on its repair method and its memory population to provide competitive results. DDECV \(+\) Repair shows evidence on the convenience of keeping a proportion of infeasible solutions in the population when solving dynamic constrained optimization problems. Finally, DDECV \(+\) Repair is highly competitive particularly when dynamism is present in both, objective function and constraints.

Keywords

Differential evolution Constraint handling Dynamic optimization Dynamic constrained optimization problem 

Notes

Acknowledgments

The first author acknowledges support from the Mexican National Council of Science and Technology (CONACyT) through a scholarship to pursue graduate studies at the University of Veracruz. The second author acknowledges support from CONACyT through Project No. 220522. This study was funded by the Mexican National Council of Science and Technology CONACyT (Grant No. 220522).

Compliance with ethical standards

Conflict of interest

María-Yaneli Ameca-Alducin declares that she has no conflict of interest. Efrén Mezura-Montes declares that he has no conflict of interest. Nicandro Cruz-Ramírez declares that he has no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • María-Yaneli Ameca-Alducin
    • 1
  • Efrén Mezura-Montes
    • 1
  • Nicandro Cruz-Ramírez
    • 1
  1. 1.Artificial Intelligence Research CenterUniversity of VeracruzXalapaMexico

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