<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Designing an efficient optimization method which also has a simple structure is generally required by users for its applications to a wide range of practical problems. In this research, an enhanced differential evolution algorithm with adaptation of switching crossover strategy (DEASC) is proposed as a general-purpose population-based optimization method for continuous optimization problems. DEASC extends the solving ability of a basic differential evolution algorithm (DE) whose performance significantly depends on user selection of the control parameters: scaling factor, crossover rate and population size. Like the original DE, the proposed method is aimed at e ciency, simplicity and robustness. The appropriate population size is selected to work in accordance with good choices of the scaling factors. Then, the switching crossover strategy of using low or high crossover rates are incorporated and adapted to suit the problem being solved. In this manner, the adaptation strategy is just a convenient add-on mechanism. To verify the performance of DEASC, it is tested on several benchmark problems of various types and di culties, and compared with some well-known methods in the literature. It is also applied to solve some practical systems of nonlinear equations. Despite its much simpler algorithmic structure, the experimental results show that DEASC greatly enhances the basic DE. It is able to solve all the test problems with fast convergence speed and overall outperforms the compared methods which have more complicated structures. In addition, DEASC also shows promising results on high dimensional test functions.</p></abstract>

Designing an efficient optimization method which also has a simple structure is generally required by users for its applications to a wide range of practical problems. In this research, an enhanced differential evolution algorithm with adaptation of switching crossover strategy (DEASC) is proposed as a general-purpose population-based optimization method for continuous optimization problems. DEASC extends the solving ability of a basic differential evolution algorithm (DE) whose performance significantly depends on user selection of the control parameters: scaling factor, crossover rate and population size. Like the original DE, the proposed method is aimed at e ciency, simplicity and robustness. The appropriate population size is selected to work in accordance with good choices of the scaling factors. Then, the switching crossover strategy of using low or high crossover rates are incorporated and adapted to suit the problem being solved. In this manner, the adaptation strategy is just a convenient add-on mechanism. To verify the performance of DEASC, it is tested on several benchmark problems of various types and di culties, and compared with some well-known methods in the literature. It is also applied to solve some practical systems of nonlinear equations. Despite its much simpler algorithmic structure, the experimental results show that DEASC greatly enhances the basic DE. It is able to solve all the test problems with fast convergence speed and overall outperforms the compared methods which have more complicated structures. In addition, DEASC also shows promising results on high dimensional test functions.

An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization

Deparment of Mathematics, Faculty of Science

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Designing an efficient optimization method which also has a simple structure is generally required by users for its applications to a wide range of practical...

An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization

Pubblicato online: 29 giu 2020

Pagine: 97 - 124

Ricevuto: 07 feb 2020

Accettato: 16 mag 2020

DOI: https://doi.org/10.2478/fcds-2020-0007

Parole chiave
Continuous optimization, enhanced differential evolution algorithm, control parameter adaptation, switching crossover strategy

© 2020 Pikul Puphasuk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization

Pubblicato online: 29 giu 2020

Pagine: 97 - 124

Ricevuto: 07 feb 2020

Accettato: 16 mag 2020

DOI: https://doi.org/10.2478/fcds-2020-0007

Parole chiaveContinuous optimization, enhanced differential evolution algorithm, control parameter adaptation, switching crossover strategy

© 2020 Pikul Puphasuk et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
Continuous optimization, enhanced differential evolution algorithm, control parameter adaptation, switching crossover strategy