Limits...
An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies.

Xiang WL, Meng XL, An MQ, Li YZ, Gao MX - Comput Intell Neurosci (2015)

Bottom Line: In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively.Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE.In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions.

View Article: PubMed Central - PubMed

Affiliation: School of Traffic & Transportation, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China.

ABSTRACT
Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions.

Show MeSH

Related in: MedlinePlus

Convergence performance of DE and EDE on the twelve test functions at D = 30.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4644554&req=5

fig1: Convergence performance of DE and EDE on the twelve test functions at D = 30.

Mentions: For the purpose of validating the enhancing effectiveness of EDE, EDE is first compared with canonical DE in terms of best, worst, median, mean, and standard deviation (Std.) values of solutions achieved by each algorithm in 30 independent runs. The corresponding results are listed in Table 2. Furthermore, the Wilcoxon rank sum test is conducted to compare the significant difference between DE and EDE at α = 0.05 significance level. The related test results are also reported in Table 2. And then, some representatives of convergence curves of DE and EDE are shown in Figure 1 in order to show the convergence speed of EDE more clearly.


An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies.

Xiang WL, Meng XL, An MQ, Li YZ, Gao MX - Comput Intell Neurosci (2015)

Convergence performance of DE and EDE on the twelve test functions at D = 30.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4644554&req=5

fig1: Convergence performance of DE and EDE on the twelve test functions at D = 30.
Mentions: For the purpose of validating the enhancing effectiveness of EDE, EDE is first compared with canonical DE in terms of best, worst, median, mean, and standard deviation (Std.) values of solutions achieved by each algorithm in 30 independent runs. The corresponding results are listed in Table 2. Furthermore, the Wilcoxon rank sum test is conducted to compare the significant difference between DE and EDE at α = 0.05 significance level. The related test results are also reported in Table 2. And then, some representatives of convergence curves of DE and EDE are shown in Figure 1 in order to show the convergence speed of EDE more clearly.

Bottom Line: In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively.Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE.In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions.

View Article: PubMed Central - PubMed

Affiliation: School of Traffic & Transportation, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China.

ABSTRACT
Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions.

Show MeSH
Related in: MedlinePlus