HCO-Based RFID Network Planning

Wang, Jun; Chen, Jinsong; Liu, Qianying; Liu, Jia

doi:10.1007/978-981-13-2829-9_40

Cited by 1 publication

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It demonstrates outstanding performance in maintaining diversity and avoiding the realization of local optimal when solving single-objective optimization problems. 13,14 Moreover, it obtains good balance among multiple quality objectives when solving recommendation system problems. 15 Prior studies have laid a strong foundation for extending the applications of the original HCO to solve complex real-world problems.…”

Section: Introductionmentioning

confidence: 99%

“…The hydrological cycle optimization (HCO) algorithm is an emerging metaheuristic algorithm. It demonstrates outstanding performance in maintaining diversity and avoiding the realization of local optimal when solving single‐objective optimization problems 13,14 . Moreover, it obtains good balance among multiple quality objectives when solving recommendation system problems 15 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hydrological cycling optimization‐based multiobjective feature‐selection method for customer segmentation

Song

Liu²,

Liu

et al. 2021

Int J Intell Syst

Self Cite

View full text Add to dashboard Cite

In the customer segmentation problem, a large number of features are manually designed and used to comprehensively describe the customer instances. However, some of these features are irrelevant, redundant, and noisy, which are not necessary and effective for customer segmentation. Feature selection is an important data preprocessing method by selecting important features from the original feature set. Particularly, feature selection in customer segmentation is a multiobjective problem that aims to minimize the feature number and maximize the classification performance. This paper proposes a multiobjective feature‐selection method based on a meta‐heuristic algorithm—hydrological cycling optimization (HCO)—to solve customer segmentation. The proposed method is able to automatically evolve a set of non‐dominated solutions that select small numbers of features and achieve high classification accuracy. To this end, three strategies based on the global flow operator, possibility‐based acceptance criteria, and density‐based evaporation and precipitation are proposed to improve the global search ability and the solution diversity of the proposed approach. The performance of the proposed approach is examined on three customer‐segmentation datasets and compared with original multiobjective HCO and six well‐known evolutionary multiobjective algorithms. The results confirm the superiority of the proposed approach in solving multiobjective customer‐segmentation problems by achieving higher calculation stability, search diversity, and solution quality compared with the other competing methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%