A multi-objective worker selection scheme in crowdsourced platforms using NSGA-II

Yadav, Akash; Mishra, Sumit; Sairam, Ashok Singh

doi:10.1016/j.eswa.2022.116991

Cited by 22 publications

(3 citation statements)

References 15 publications

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“…Nath et al [27] formulated the multi-objective RRAP with different structures and solved it using NSGA-II and NSGA-III algorithms. To perform a specific task by optimizing the collective expertise and team cost, Yadav et al [28] selected the best team of workers using their proposed NSGA-II based algorithm. Brentan et al [29] found out the optimal solutions of the MOOP of water quality sensor placement using NSGA-II.…”

Section: Related Workmentioning

confidence: 99%

Optimization of time-dependent fuzzy multi-objective reliability redundancy allocation problem for n-stage series–parallel system

De,

Roy,

Roy

et al. 2023

Innovations Syst Softw Eng

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This study introduces a time dependent fuzzy multi-objective reliability redundancy allocation problem (TF-MORRAP) for the n-stage (level) series parallel system. System reliability maximization and system cost minimization according to time by optimizing the redundant components counting at every stage of the system is the main objective of this study. This optimization is done by satisfying the entropy constraints with limited redundant components at every stage and in the whole system. The reliability and cost of every component are represented as triangular fuzzy numbers (TFN) to handle the uncertainty of input information of the system. According to time the component reliability and cost decrease by some factor of their previous existing value. This factor follows the change in the length of radius of the inverse logarithmic spiral with respect to angle which is regarded as time here. The proposed problem is analysed by using an over-speed protection system of a gas turbine. We compare the membership values of optimal solutions obtained by using two well-known techniques namely Non-dominated sorting genetic algorithm-II (NSGA-II) and a multi-objective particle swarm optimization algorithm called NF-MOPSO. Various performances of the algorithms are compared to solve the aforementioned problem by using some performance metrics. NF-MOPSO shows the high satisfaction level of objective functions and better performance than NSGA-II.

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Section: Related Workmentioning

confidence: 99%

Optimization of time-dependent fuzzy multi-objective reliability redundancy allocation problem for n-stage series–parallel system

De,

Roy,

Roy

et al. 2023

Innovations Syst Softw Eng

View full text Add to dashboard Cite

show abstract

“…NSGA-II easily become trapped around a local optimal solution (Yadav et al, 2022). Search directions toward better values are retained with high probability, and search directions toward poor values are retained with a lower probability.…”

Section: Improving the Search Ability Of Nsga-iimentioning

confidence: 99%

Data-driven method of solving computationally expensive combined economic/emission dispatch problems in large-scale power systems: an improved kriging-assisted optimization approach

Lin,

Liang,

Pang

et al. 2023

Front. Energy Res.

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Combined economic/emission dispatch (CEED) is generally studied using analytical objective functions. However, for large-scale, high-dimension power systems, CEED problems are transformed into computationally expensive CEED (CECEED) problems, for which existing approaches are time-consuming and may not obtain satisfactory solutions. To overcome this problem, a novel data-driven surrogate-assisted method is introduced firstly. The fuel cost and emission objective functions are replaced by improved Kriging-based surrogate models. A new infilling sampling strategy for updating Kriging-based surrogate models online is proposed, which improves their fitting accuracy. Through this way, the evaluation time of the objective functions is significantly reduced. Secondly, the optimization of CECEED is executed by an improved non-dominated sorting genetic algorithm-II (NSGA-II). The above infilling sampling strategy is also used to reduce the number of evaluations for original mathematic fitness functions. To improve their local convergence ability and global search abilities, the individuals that exhibit excellent performance in a single objective are cloned and mutated. Finally, information about the Pareto front is used to guide individuals to search for better solutions. The effectiveness of this optimization method is demonstrated through simulations of IEEE 118-bus test system and IEEE 300-bus test system.

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“…Amazon mechanical Turk 2 (AMT) is a routine activity platform used all over the world. In order to ensure the quality of the data collected, it is possible to act upstream of the campaign by selecting the contributors, as Yadav et al (2022) does. Another possibility is to consider data processing after the campaign.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the qualification and behavior of a contributor and aggregation of his answers in a crowdsourcing context

Thierry¹,

Martin²,

Dubois³

et al. 2023

Preprint

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Crowdsourcing is the outsourcing of tasks to a crowd of contributors on a dedicated platform. The crowd on these platforms is very diversified and includes various profiles of contributors which generates data of uneven quality. However, majority voting, which is the aggregating method commonly used in platforms, gives equal weight to each contribution. To overcome this problem, we propose a method, MONITOR, which estimates the contributor's profile and aggregates the collected data by taking into account their possible imperfections thanks to the theory of belief functions. To do so, MONITOR starts by estimating the profile of the contributor through his qualification for the task and his behavior.Crowdsourcing campaigns have been carried out to collect the necessary data to test MONITOR on real data in order to compare it to existing approaches.The results of the experiments show that thanks to the use of the MONITOR method, we obtain a better rate of correct answer after aggregation of the contributions compared to the majority voting. Our contributions in this article are for the first time the proposal of a model that takes into account both the qualification of the contributor and his behavior in the estimation of his profile.For the second one, the weakening and the aggregation of the answers according to the estimated profiles.

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A multi-objective worker selection scheme in crowdsourced platforms using NSGA-II

Cited by 22 publications

References 15 publications

Optimization of time-dependent fuzzy multi-objective reliability redundancy allocation problem for n-stage series–parallel system

Optimization of time-dependent fuzzy multi-objective reliability redundancy allocation problem for n-stage series–parallel system

Data-driven method of solving computationally expensive combined economic/emission dispatch problems in large-scale power systems: an improved kriging-assisted optimization approach

Estimation of the qualification and behavior of a contributor and aggregation of his answers in a crowdsourcing context

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