APDDE: self-adaptive parameter dynamics differential evolution algorithm

Wang, Hongbo; Ren, Xuena; Li, Guoqiang; Tu, Xuyan

doi:10.1007/s00500-016-2418-1

Cited by 13 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DE/current − to − p best/1 is one of the most successful mutation operators because of its relatively balanced performance between exploration and exploitation. Wang et al [ 12 ] proposed a new mutation strategy called DE/current − to − l best/1 based on the values near the current parameter to keep balance of exploitation and exploration capabilities during the differential evolution. A novel DE algorithm with intersect mutation operation called intersect mutation differential evolution (IMDE) was proposed [ 13 ] to further improve the performance of the standard DE algorithm.…”

Section: Related Workmentioning

confidence: 99%

An External Selection Mechanism for Differential Evolution Algorithm

Zhang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The procedures of differential evolution algorithm can be summarized as population initialization, mutation, crossover, and selection. However, successful solutions generated by each iteration have not been fully utilized to our best knowledge. In this study, an external selection mechanism (ESM) is presented to improve differential evolution (DE) algorithm performance. The proposed method stores successful solutions of each iteration into an archive. When the individual is in a state of stagnation, the parents for mutation operation are selected from the archive to restore the algorithm’s search. Most significant of all, a crowding entropy diversity measurement in fitness landscape is proposed, cooperated with fitness rank, to preserve the diversity and superiority of the archive. The ESM can be integrated into existing algorithms to improve the algorithm’s ability to escape the situation of stagnation. CEC2017 benchmark functions are used for verification of the proposed mechanism’s performance. Experimental results show that the ESM is universal, which can improve the accuracy of DE and its variant algorithms simultaneously.

show abstract

Section: Related Workmentioning

confidence: 99%

An External Selection Mechanism for Differential Evolution Algorithm

Zhang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Yintong Li [20] further proposed a new directional mutation strategy to ensure that the evolution progresses in a better direction. Wang [21] introduced the DE/current-to-lpbest/1 mutation strategy, which leverages a blend of the current individual's information and the elite solutions. This approach involves guiding the mutation process with insights from multiple elite individuals, enhancing the algorithm's ability to explore potential areas more effectively.…”

Section: Introductionmentioning

confidence: 99%

Improving Dual-Population Differential Evolution Based on Hierarchical Mutation and Selection Strategy

Huang,

Qian,

Song

2023

Electronics

View full text Add to dashboard Cite

The dual-population differential evolution (DDE) algorithm is an optimization technique that simultaneously maintains two populations to balance global and local search. It has been demonstrated to outperform single-population differential evolution algorithms. However, existing improvements to dual-population differential evolution algorithms often overlook the importance of selecting appropriate mutation and selection operators to enhance algorithm performance. In this paper, we propose a dual-population differential evolution (DPDE) algorithm based on a hierarchical mutation and selection strategy. We divided the population into elite and normal subpopulations based on fitness values. Information exchange between the two subpopulations was facilitated through a hierarchical mutation strategy, promoting a balanced exploration–exploitation trade-off in the algorithm. Additionally, this paper presents a new hierarchical selection strategy aimed at improving the population’s capacity to avoid local optima. It achieves this by accepting discarded trial vectors differently compared to previous methods. We expect that the newly introduced hierarchical selection and mutation strategies will work in synergy, effectively harnessing their potential to enhance the algorithm’s performance. Extensive experiments were conducted on the CEC 2017 and CEC 2011 test sets. The results showed that the DPDE algorithm offers competitive performance, comparable to six state-of-the-art differential evolution algorithms.

show abstract

“…They used an adaptive scheme to control the scaling factor and the crossover rate. [51] introduced a self-adaptive differential evolution algorithm (APDDE). The algorithm integrates the detecting values into two mutation strategies to produce the offspring population.…”

Section: Introductionmentioning

confidence: 99%

An improved adaptive memetic differential evolution optimization algorithms for data clustering problems

et al. 2019

View full text Add to dashboard Cite

The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm.

show abstract

APDDE: self-adaptive parameter dynamics differential evolution algorithm

Cited by 13 publications

References 22 publications

An External Selection Mechanism for Differential Evolution Algorithm

An External Selection Mechanism for Differential Evolution Algorithm

Improving Dual-Population Differential Evolution Based on Hierarchical Mutation and Selection Strategy

An improved adaptive memetic differential evolution optimization algorithms for data clustering problems

Contact Info

Product

Resources

About