The PSK Method for Solving Fully Intuitionistic Fuzzy Assignment Problems With Some Software Tools

Kumar, P. Senthil

doi:10.4018/978-1-5225-8458-2.ch009

Cited by 17 publications

(3 citation statements)

References 54 publications

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“…In addition, the fuzzy set-based optimization algorithm [70] has received much attention in recent years and has been successfully applied to solid assignment [71,72] and transportation [73] problems, and it can also be considered a focal method for future studies on high-order SNP epistatic interaction detection. In addition, it is also very important to develop an effective scoring function for seeking clues to guide the SISA to locate the positions of potential SNP interactions.…”

Section: Discussionmentioning

confidence: 99%

MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions

Tuo

Liu

et al. 2022

Complex Intell. Syst.

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Genome-wide association studies have succeeded in identifying genetic variants associated with complex diseases, but the findings have not been well interpreted biologically. Although it is widely accepted that epistatic interactions of high-order single nucleotide polymorphisms (SNPs) [(1) Single nucleotide polymorphisms (SNP) are mainly deoxyribonucleic acid (DNA) sequence polymorphisms caused by variants at a single nucleotide at the genome level. They are the most common type of heritable variation in humans.] are important causes of complex diseases, the combinatorial explosion of millions of SNPs and multiple tests impose a large computational burden. Moreover, it is extremely challenging to correctly distinguish high-order SNP epistatic interactions from other high-order SNP combinations due to small sample sizes. In this study, a multitasking harmony search algorithm (MTHSA-DHEI) is proposed for detecting high-order epistatic interactions [(2) In classical genetics, if genes X1 and X2 are mutated and each mutation by itself produces a unique disease status (phenotype) but the mutations together cause the same disease status as the gene X1 mutation, gene X1 is epistatic and gene X2 is hypostatic, and gene X1 has an epistatic effect (main effect) on disease status. In this work, a high-order epistatic interaction occurs when two or more SNP loci have a joint influence on disease status.], with the goal of simultaneously detecting multiple types of high-order (k1-order, k2-order, …, kn-order) SNP epistatic interactions. Unified coding is adopted for multiple tasks, and four complementary association evaluation functions are employed to improve the capability of discriminating the high-order SNP epistatic interactions. We compare the proposed MTHSA-DHEI method with four excellent methods for detecting high-order SNP interactions for 8 high-orderepistatic interaction models with no marginal effect (EINMEs) and 12 epistatic interaction models with marginal effects (EIMEs) (*) and implement the MTHSA-DHEI algorithm with a real dataset: age-related macular degeneration (AMD). The experimental results indicate that MTHSA-DHEI has power and an F1-score exceeding 90% for all EIMEs and five EINMEs and reduces the computational time by more than 90%. It can efficiently perform multiple high-order detection tasks for high-order epistatic interactions and improve the discrimination ability for diverse epistasis models.

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Section: Discussionmentioning

confidence: 99%

MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions

Tuo

Liu

et al. 2022

Complex Intell. Syst.

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“…The clustering algorithm in the FANET divides the UAVs into several clusters, with each cluster composed of a cluster head (CH) and a number of cluster members (CMs), and an allocation algorithm is needed to assign several finite number of objects to the cluster according to the task [ 11 – 13 ]. In such a configuration, only the CH takes responsibility for intra-cluster and inter-cluster communication in a higher level network, while the CMs in the cluster only need to complete the intra-cluster communication in a lower level network [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Firefly swarm intelligence based cooperative localization and automatic clustering for indoor FANETs

et al. 2023

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At present, the applications of multiple unmanned aerial vehicles (UAVs) are becoming more and more widespread, covering many civil and military fields. When performing tasks, UAVs will form a flying ad hoc network (FANET) to communicate to each other. However, subject to high mobility, dynamic topology, and limited energy of FANETs, maintaining stable communication performance is a challenging task. As a potential solution, the clustering routing algorithm divides the entire network into multiple clusters to achieve strong network performance. Meanwhile, the accurate localization of UAV is also strongly required when FANETs are applied in the indoor scenario. In this paper, we propose a firefly swarm intelligence based cooperative localization (FSICL) and automatic clustering (FSIAC) for FANETs. Firstly, we combine the firefly algorithm (FA) and Chan algorithm to better cooperative locate the UAVs. Secondly, we propose the fitness function consisting of link survival probability, node degree-difference, average distance, and residual energy, and take it as the light intensity of the firefly. Thirdly, the FA is put forward for cluster-head (CH) selection and cluster formation. Simulation results indicate that the proposed FSICL algorithm achieves the higher localization accuracy faster, and the FSIAC algorithm achieves the higher stability of clusters, longer link expiration time (LET), and longer node lifetime, all of which improve the communication performance for indoor FANETs.

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“…Due to this special features of intuitionistic fuzzy set theory, Angelov (1997) introduced the concept of optimization problems under intuitionistic fuzzy environment. Subsequently, several authors (Hussain and Kumar (2012a, b), Hussain (2014a, b, c, d, 2016a), Nagoorgani et al (2016), Jinshuai et al (2017), Abhishekh and Nishad (2018), Kumar (2018bKumar ( , c, d, 2019aKumar ( , 2020a) have introduced various algorithms for solving optimization problems under intuitionistic fuzzy environment. In particular, both the balanced and unbalanced intuitionistic fuzzy assignment problems was solved by Kumar and Hussain (2016b, c).…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic fuzzy solid assignment problems: a software-based approach

Kumar

2019

Int J Syst Assur Eng Manag

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The PSK Method for Solving Fully Intuitionistic Fuzzy Assignment Problems With Some Software Tools

Cited by 17 publications

References 54 publications

MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions

MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions

Firefly swarm intelligence based cooperative localization and automatic clustering for indoor FANETs

Intuitionistic fuzzy solid assignment problems: a software-based approach

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