Multiobjective multitasking optimization assisted by multidirectional prediction method

Dang, Qianlong; Gao, Weifeng; Gong, Maoguo

doi:10.1007/s40747-021-00624-2

Cited by 9 publications

(3 citation statements)

References 35 publications

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“…To solve scheduling problems with batch distribution, Xu et al presented multitasking optimization [47]. Gao et al designed a transfer strategy based on the multidirectional prediction method to improve the performance of the multiobjective multitasking optimization approach [48]. Zhao et al proposed a polynomial regression surface modelling approach based on multitasking optimization for rational basis function selection [49].…”

Section: Multitasking Optimization Modelmentioning

confidence: 99%

“…Zhao et al proposed a polynomial regression surface modelling approach based on multitasking optimization for rational basis function selection [49]. EMO can efficiently address multiple different optimization problems simultaneously, enhance the global search ability and improve the performance of each task via knowledge transfer between tasks [48].…”

Section: Multitasking Optimization Modelmentioning

confidence: 99%

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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: Multitasking Optimization Modelmentioning

confidence: 99%

Section: Multitasking Optimization Modelmentioning

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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“…Inspired by the intelligent behaviors of social animals, Eberhart and Kennedy [30] proposed PSO, where a swarm of particles traverses the whole solution space to find the global optimum. PSO is a widely used evolutionary computation algorithm [36]. In PSO, each particle presents a candidate solution in the swarm.…”

Section: Particle Swarm Optimizermentioning

confidence: 99%

A swarm-optimizer-assisted simulation and prediction model for emerging infectious diseases based on SEIR

Shi

Wei

Chen

2022

Complex Intell. Syst.

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Mechanism-driven models based on transmission dynamics and statistic models driven by public health data are two main methods for simulating and predicting emerging infectious diseases. In this paper, we intend to combine these two methods to develop a more comprehensive model for the simulation and prediction of emerging infectious diseases. First, we combine a standard epidemic dynamic, the susceptible–exposed–infected–recovered (SEIR) model with population migration. This model can provide a biological spread process for emerging infectious diseases. Second, to determine suitable parameters for the model, we propose a data-driven approach, in which the public health data and population migration data are assembled. Moreover, an objective function is defined to minimize the error based on these data. Third, based on the proposed model, we further develop a swarm-optimizer-assisted simulation and prediction method, which contains two modules. In the first module, we use a level-based learning swarm optimizer to optimize the parameters required in the epidemic mechanism. In the second module, the optimized parameters are used to predicate the spread of emerging infectious diseases. Finally, various experiments are conducted to validate the effectiveness of the proposed model and method.

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Dual transfer learning with generative filtering model for multiobjective multitasking optimization

Dang

Gao²,

Gong³

2022

Memetic Comp.

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Multiobjective multitasking optimization assisted by multidirectional prediction method

Cited by 9 publications

References 35 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

A swarm-optimizer-assisted simulation and prediction model for emerging infectious diseases based on SEIR

Dual transfer learning with generative filtering model for multiobjective multitasking optimization

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