Comparative Study of Five Metaheuristic Algorithms for Team Formation Problem

Kader, Md. Abdul; Zamli, Kamal Z.

doi:10.1007/978-981-16-4115-2_10

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…In 2021 in [31], The author concentrated on a distinct idea, graph reduction, which condenses the massive data to just the experts and the required skills, enabling the quick extraction of experts for collaboration. In 2022 in [32], the slap swarm algorithm (SSA), the owl search algorithm (OSA), the sooty tern optimization algorithm (STOA), the squirrel search algorithm (SqSA), and the crow search algorithm are five metaheuristic methods the author uses to tackle the team formation problem (CSA). The analysis takes into consideration the very minimum in terms of team costs and skills.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Jellyfish Search Optimizer for Collaborative Team Formation in Social Network

2023

IJIES

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This study presents an enhanced model based on a new swarm intelligence algorithm called jellyfish search optimizer (JSO). The suggested model is called chaotic jellyfish search with enhanced swap operator (CJSESOS). The CJSESOS algorithm has two modifications to the original JSO algorithm, the first is the chaotic sequence generated by iterating a logistic map which is named CJSO. This enhancement discovers a creative solution by directing particles to different location of the search space, the second is enhanced swap sequence operator which increased the CJSO algorithm's ability to escape from local minimum by diversifying the results. The performance of the CJSESOS is evaluated using fourteen benchmark functions. The proposed model is applied to solve a discreate real life problem, named team formation (TF) problem which considered one of the most significant problems in computer science and optimization. TF problem is defined as creating the most effective team of experts in social network to carry out a task with the lowest possible cost. The proposed CJSESOS algorithm was tested for solving the TF problem with varying number of skills in different datasets. In addition, the proposed algorithm is compared to well-known optimization algorithms such as jellyfish search optimizer (JSO), particle swarm optimization (PSO), genetic algorithm (GA), grey wolf optimization (GWO), heap based optimizer (HBO), aquila optimizer (AO) and pelican optimization algorithm (POA). The simulation results show that the proposed model outperformed all the compared algorithms on the term of efficiency and accuracy.

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Section: Related Workmentioning

confidence: 99%

Enhanced Jellyfish Search Optimizer for Collaborative Team Formation in Social Network

2023

IJIES

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“…Kader et al [20] have compared five metaheuristic algorithms used in the Covid19 team formation problem. Based on the ideal findings the crow search algorithm was the effective metaheuristic method.…”

Section: Related Workmentioning

confidence: 99%

Ischemic Heart Disease Prediction Using Optimized Squirrel Search Feature Selection Algorithm

2022

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In recent years, the volume in globally recognized medical data sets are increasing both with attributes and number of records. Machine learning algorithms aiming to detect and diagnose ischemic heart diseases requires high efficacy and judgment. The state of art Ischemic heart disease data sets presents several issues, including feature selection, sample size, sample imbalance, and lack of magnitude for some characteristics etc. The proposed study is primarily concerned with improving feature selection and reducing the number of features yet giving better decisions. In this study, to pick salient aspects of heart illness, an improved squirrel search optimization algorithm with a meta-heuristic approach is proposed. Comparison study of the proposed ischemic heart disease squirrel search optimization (IHDSSO) model in conjunction with random forest classifier ensures better feature selection and accuracy over 98% with respect to other state-of-the-art optimization algorithms.

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“…TFP, kaynakların, bir görev için gerekli becerilere sahip ve optimal düzeyde uyum sağlayacak bir gruba atanması problemidir. Bu, yazılım geliştiricilerin projelere [6], sağlık çalışanlarının pandemiyle mücadele gruplarına [7], taksi şoförlerinin rekabetçi gruplara [8], öğrencilerin çalışma gruplarına [9] veya oyuncuların futbol takımlarına [10] atanması olabilir. TFP yalnızca kişilerin gruplandırılması değil, görevleri yerine getirmeleri için otomatik yönlendirmeli araçların [11] ve nesnelerin internetindeki cihazların [12] gruplandırılması gibi farklı varyasyonlara sahiptir.…”

Section: Introductionunclassified

Sosyal Ağ Varlığında Takım Oluşturma Problemine Hibrit Bir Genetik Algoritma Önerisi

TÜRKKAHRAMAN

Karabulut

2023

Deu Muhendislik Fakultesi Fen Ve Muhendislik

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Zamanla karmaşıklaşan faaliyetler bireysel çalışmanın yerini takım çalışmasına bırakmasına neden olmuştur. Takımların performansını etkileyen önemli faktörlerden biri ise üyeler arasındaki uyumdur. Bu sebeple sosyal ağ varlığında takım oluşturma problemi, literatürdeki güncel problemlerden biri olmaya başlamıştır. Bu çalışmada probleme, klasik genetik algoritmanın tüm arama uzayını keşfetme gücü ile yerel arama algoritmalarının arama uzayındaki sınırlı bölgeleri inceleme gücünü birleştiren takım oluşturucu hibrit genetik algoritma önerilmektedir. Önerilen algoritma, takım oluşturma problemi için literatürde yaygınca kullanılan bir veri seti ile test edilmiş ve elde edilen sonuçlarla algoritmanın etkinliği doğrulanmıştır.

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Comparative Study of Five Metaheuristic Algorithms for Team Formation Problem

Cited by 4 publications

References 15 publications

Enhanced Jellyfish Search Optimizer for Collaborative Team Formation in Social Network

Enhanced Jellyfish Search Optimizer for Collaborative Team Formation in Social Network

Ischemic Heart Disease Prediction Using Optimized Squirrel Search Feature Selection Algorithm

Sosyal Ağ Varlığında Takım Oluşturma Problemine Hibrit Bir Genetik Algoritma Önerisi

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