<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper, we study a problem of the control parameter settings in Differential Evolution algorithm and test a novel variant of the algorithm called CoBiDE. Although Differential Evolution with basic setting (i.e., <italic>CR</italic>=0.5; <italic>F</italic> =0.5) works quite well, it starts to fail on rotated functions. In general, we want to improve the convergence of algorithm primarily on rotated functions. It is done by adapting crossover parameter <italic>CR</italic> whereas parameter F is fixed to 0.5. There is a recommendation to set <italic>CR</italic> = 1 for rotated functions. It means that trial vectors are essentially composed from mutant. However, it is not easy task to set the parameters appropriately for solving optimization problem but it is crucial for obtaining good results. Moreover, the quality of points produced in evolution is highly affected by the coordinate system. In CoBiDE, the authors proposed a new coordinate system based on the current distribution of points in the population. We test these two approaches by running both algorithms on six pairs of rotated and non-rotated functions from CEC 2013 benchmark set in two levels of dimension space. This experimental study aims to reveal if such algorithm’s setting is invariant under a rotation.</p></abstract>

In this paper, we study a problem of the control parameter settings in Differential Evolution algorithm and test a novel variant of the algorithm called CoBiDE. Although Differential Evolution with basic setting (i.e., CR=0.5; F =0.5) works quite well, it starts to fail on rotated functions. In general, we want to improve the convergence of algorithm primarily on rotated functions. It is done by adapting crossover parameter CR whereas parameter F is fixed to 0.5. There is a recommendation to set CR = 1 for rotated functions. It means that trial vectors are essentially composed from mutant. However, it is not easy task to set the parameters appropriately for solving optimization problem but it is crucial for obtaining good results. Moreover, the quality of points produced in evolution is highly affected by the coordinate system. In CoBiDE, the authors proposed a new coordinate system based on the current distribution of points in the population. We test these two approaches by running both algorithms on six pairs of rotated and non-rotated functions from CEC 2013 benchmark set in two levels of dimension space. This experimental study aims to reveal if such algorithm’s setting is invariant under a rotation.

Is Differential Evolution Rotationally Invariant?

Tatra Mountains Mathematical Publications

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Is Differential Evolution Rotationally Invariant?"}

In this paper, we study a problem of the control parameter settings in Differential Evolution algorithm and test a novel variant of the algorithm called...

Is Differential Evolution Rotationally Invariant?

Data publikacji: 12 mar 2019

Zakres stron: 155 - 165

Otrzymano: 05 gru 2017

DOI: https://doi.org/10.2478/tmmp-2018-0027

Słowa kluczowe
global optimization, differential evolution, CEC2013 benchmark (set)

© 2018 Hana Zámečníková et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Is Differential Evolution Rotationally Invariant?

Data publikacji: 12 mar 2019

Zakres stron: 155 - 165

Otrzymano: 05 gru 2017

DOI: https://doi.org/10.2478/tmmp-2018-0027

Słowa kluczoweglobal optimization, differential evolution, CEC2013 benchmark (set)

© 2018 Hana Zámečníková et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
global optimization, differential evolution, CEC2013 benchmark (set)