Limits...
An adaptive Cauchy differential evolution algorithm for global numerical optimization.

Choi TJ, Ahn CW, An J - ScientificWorldJournal (2013)

Bottom Line: Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem.The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals' parameter values by using the Cauchy distribution.Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Suwon, Republic of Korea.

ABSTRACT
Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals' parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems.

Show MeSH
Average best graphs of Adaptive Cauchy DE with the compared DE algorithms.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3713346&req=5

fig2: Average best graphs of Adaptive Cauchy DE with the compared DE algorithms.

Mentions: Figure 2 shows the average best graphs of adaptive Cauchy DE and the compared DE algorithms.


An adaptive Cauchy differential evolution algorithm for global numerical optimization.

Choi TJ, Ahn CW, An J - ScientificWorldJournal (2013)

Average best graphs of Adaptive Cauchy DE with the compared DE algorithms.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3713346&req=5

fig2: Average best graphs of Adaptive Cauchy DE with the compared DE algorithms.
Mentions: Figure 2 shows the average best graphs of adaptive Cauchy DE and the compared DE algorithms.

Bottom Line: Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem.The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals' parameter values by using the Cauchy distribution.Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Suwon, Republic of Korea.

ABSTRACT
Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals' parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems.

Show MeSH