An adaptive clustering-based evolutionary algorithm for many-objective optimization problems

Liu, Songbai; Yu, Qiyuan; Lin, Qiuzhen; Tan, Kay Chen

doi:10.1016/j.ins.2020.03.104

Cited by 36 publications

(5 citation statements)

References 42 publications

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“…where the neighbor set of pi includes T solutions with the closest diversity distance in (13) to itself. Note that T is set as 0.1 N in this paper as suggested in [49]- [50]. In addition, due to page limitations, a parameter sensitivity analysis of T is given in Table S1 of supplementary file.…”

Section: The Convergence and Diversity Estimation Strategymentioning

confidence: 99%

A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization

Lin

et al. 2022

Engineering Applications of Artificial Intelligence

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Section: The Convergence and Diversity Estimation Strategymentioning

confidence: 99%

A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization

Lin

et al. 2022

Engineering Applications of Artificial Intelligence

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“…MaOEA/AC [12] is a many-objective evolutionary algorithm that uses an adaptive clustering-based selection strategy. The population of solutions is divided into N clusters.…”

Section: E Maoea/acmentioning

confidence: 99%

“…To enhance the exploitative capability of GDE3, we incorporate a local search strategy, which improves the updated solution at hand by searching and replacing it with a better solution in the neighborhood. We have done experiments using DNA sequence data sets of different size and compared the results of our proposed MGDE3 with well cited multi and many-objective evolutionary algorithms (MaOEAs) like NSGA-III [6], NSGA-II [7], MOEA/D [8], DBEA [9], GDE3 [10], RVEA [11], and MaOEA/AC [12]. The results show that MGDE3 has better performance than the compared methods.…”

Section: Introductionmentioning

confidence: 99%

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

2020

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Designing reliable sequences of DNA (Deoxyribonucleic Acid) is a critical task in the fields of DNA computing, and nanotechnology. The quality and reliability of the DNA sequence can directly affect the accuracy of the processing of information stored in sequences. This problem of designing reliable sequences belongs to the NP-hard class of problems. It has many incompatible design criteria, which cannot be optimized at the same time. Many objective evolutionary algorithms can balance conflicting design criteria by using a diverse population of solutions. This paper proposes an opposition-based Memetic Generalized Differential Evolution (MGDE3) to handle four conflicting design criteria for reliable DNA sequence design. Opposition-based learning and local search strategies are suggested to strengthen the explorative and exploitative properties of the proposed MGDE3. The proposed algorithm is bench-marked with small, medium, and large data sets against 7 highly-cited many-objective and multi-objective algorithms. Experimental results and statistical analysis reveal that MGDE3 significantly outperforms the compared algorithms. The proposed method generates reliable real-life sequences of DNA that are substantially better than the DNA sequences generated by other considered algorithms.

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“…There are errors in other parameters calculated by the geometric reasoning method, and the feature recognition system based on a prompt requires prompt features in the workpiece. Therefore, 3D vision technology must be used for deep and accurate machining [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Enhanced curve-based segmentation method for point clouds of curved and irregular structures

Song,

Zhang,

et al. 2024

Meas. Sci. Technol.

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This paper proposes an improved method for model-based segmentation of curved and irregular mounded structures in 3D measurements. The proposed method divides the point cloud data into several levels according to the reasonable width calculated from the density of points, and then fits a curve model with 2D points to each level separately. The classification results of specific types are merged to obtain specific structural measurement data in 3D space. Experiments were conducted on the proposed method using the region growth algorithm (SRG) and the model-based segmentation method (MS) provided in the PCL library as the control group. The results show that the proposed method achieves higher accuracy with a mean intersection merge ratio (MloU) of more than 0.8238, which is at least37.92% higher than SRG and MS. The proposed method is also faster with a time-consuming only 1/5 of SRG and 1/2 of MS. Therefore, the proposed method is an effective and efficient way to segment the measurement data of curved and irregular mounded structures in 3D measurements. The method proposed in this paper has also applied in the practical robotic grinding task, the root mean square error of the grinding amount is less than 2 mm, and good grinding results are achieved.grinding results are achieved.

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An adaptive clustering-based evolutionary algorithm for many-objective optimization problems

Cited by 36 publications

References 42 publications

A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization

A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

Enhanced curve-based segmentation method for point clouds of curved and irregular structures

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