Data clustering using leaders and followers optimization and differential evolution

Zorarpacı, Ezgi

doi:10.1016/j.asoc.2022.109838

Cited by 4 publications

(1 citation statement)

References 74 publications

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“…Unlike traditional optimization algorithms, intelligent optimization algorithms have been proved to be one of the most effective methods to resolve such kind of complex engineering problems [5][6][7][8][9]. Due to its simple but robust structure, and few requirement of control parameters, DE algorithm [8] has been widely and successfully applied in the fields of clustering [10][11][12], neural networks [13][14][15], economic load dispatch [16], and so on. Although the above-mentioned extensive application results demonstrate the powerful search capability and application context of DE, it still suffers from falling into local optima [5].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Adaptive Differential Evolution Algorithm With Multi-Mutation Schemes and Weighted Control Parameter Setting

Tian,

Meng,

et al. 2023

IEEE Access

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Differential evolution (DE) algorithm is one of the most effective and efficient heuristic approaches for solving complex black box problems. But it still easily suffers from premature convergence and stagnation. To alleviate these defects, this paper presents a novel DE variant, named enhanced adaptive differential evolution algorithm with multi-mutation schemes and weighted control parameter setting (MWADE), to further strengthen its search capability. In MWADE, a multi-schemes mutation strategy is first proposed to properly exploit or explore the promising information of each individual. Herein, the whole population are dynamically grouped into three subpopulations according to their fitness values and search performance, and three different mutant operators with various search characteristics are respectively adopted for each subpopulation. Meanwhile, in order to ensure the exploration of algorithm at the later evolutionary stage, a weight-controlled parameter setting is proposed to suitably assign scale factors for different differential vectors. Moreover, a random opposition mechanism with greedy selection is introduced to avoid trapping in local optima or stagnation, and an adaptive population size reduction scheme is devised to further promote the search effectiveness of algorithm. Finally, to illustrate the performance of MWADE, thirteen typical algorithms are adopted and compared with MWADE on 30 functions from IEEE CEC 2017 test suite with different dimensions, and the effectiveness of its proposed components are also investigated. Numerical results indicate that the proposed algorithm has a better search performance.

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