Self-adaptive differential evolution based on best and mean schemes

Jamil, Nurul Aini; Wang, Shir Li; Ng, Theam Foo

doi:10.1109/iccsce.2015.7482199

Cited by 2 publications

(1 citation statement)

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“…[36] proposed a self-adaptive differential evolution algorithm with a hybrid mutation operator (SHDE) for parameters identification problem. In [52], researchers proposed a self-adaptive DE which can predict the control parameters based on the ensemble. [53] proposed a self-adaptive DE algorithm with discrete mutation control parameters (DMPSADE).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Enhanced Connectivity Validity Measure Based on Outlier Detection for Multi-Objective Metaheuristic Data Clustering Algorithms

Mustafa

Ayob

2022

Applied Computational Intelligence and Soft Computing

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Data clustering algorithms experience challenges in identifying data points that are either noise or outlier. Hence, this paper proposes an enhanced connectivity measure based on the outlier detection approach for multi-objective data clustering problems. The proposed algorithm aims to improve the quality of the solution by utilising the local outlier factor method (LOF) with the connectivity validity measure. This modification is applied to select the neighbour data point’s mechanism that can be modified to eliminate such outliers. The performance of the proposed approach is assessed by applying the multi-objective algorithms to eight real-life and seven synthetic two-dimensional datasets. The external validity is evaluated using the F-measure, while the performance assessment matrices are employed to assess the quality of Pareto-optimal sets like the coverage and overall non-dominant vector generation. Our experimental results proved that the proposed outlier detection method has enhanced the performance of the multi-objective data clustering algorithms.

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Self-adaptive differential evolution based on best and mean schemes

Cited by 2 publications

References 10 publications

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

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

Enhanced Connectivity Validity Measure Based on Outlier Detection for Multi-Objective Metaheuristic Data Clustering Algorithms

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