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Solving team making problem for crowdsourcing with hybrid metaheuristic algorithm

Published: 06 July 2018 Publication History

Abstract

For a typical crowdsourcing process, a task publisher first publishes a task with an acceptable budget. Then hundreds of crowdsourced workers apply for the task with their desired bids. To recruit an adequate Crowdsourced Virtual Team (CVT) while balancing the profits of the task publisher and crowdsourced workers, previous studies proposed various algorithms, including Genetic Algorithm (GA), Alternating Variable Method (AVM), etc. However, the performance is still limited. In this study, we propose a novel hybrid metaheuristic algorithm CVTMaker to help publishers identify ideal CVTs. CVTMaker is effective which combines (1+1) Evolutionary Strategy ((1+1)-ES) and AVM to search solutions. Experimental results show that CVTMaker significantly outperforms GA and AVM over 3,117 and 5,642 of the 6,000 instances respectively.

References

[1]
Andrea Arcuri. 2013. It really does matter how you normalize the branch distance in search-based software testing. Software Testing, Verification and Reliability 23, 2 (2013), 119--147.
[2]
He Jiang, Zhilei Ren, Xiaochen Li, and Xiaochen Lai. 2015. Transformed Search Based Software Engineering: A New Paradigm of SBSE. In International Symposium on Search Based Software Engineering. Springer, 203--218.
[3]
Tao Yue, Shaukat Ali, and Shuai Wang. 2015. An evolutionary and automated virtual team making approach for crowdsourcing platforms. In Crowdsourcing. Springer, 113--130.

Cited By

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  • (2023)A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software EngineeringComplexity10.1155/2023/45775812023Online publication date: 1-Jan-2023
  • (2023)Mantis Search Algorithm: A novel bio-inspired algorithm for global optimization and engineering design problemsComputer Methods in Applied Mechanics and Engineering10.1016/j.cma.2023.116200415(116200)Online publication date: Oct-2023
  • (2022)Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applicationsComputer Methods in Applied Mechanics and Engineering10.1016/j.cma.2021.114194388(114194)Online publication date: Jan-2022

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  1. Solving team making problem for crowdsourcing with hybrid metaheuristic algorithm

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    cover image ACM Conferences
    GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
    July 2018
    1968 pages
    ISBN:9781450357647
    DOI:10.1145/3205651
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 06 July 2018

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    Author Tags

    1. crowdsourcing
    2. evolution strategy
    3. hybrid meta-heuristic algorithm
    4. local search
    5. virtual team making

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    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    View all
    • (2023)A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software EngineeringComplexity10.1155/2023/45775812023Online publication date: 1-Jan-2023
    • (2023)Mantis Search Algorithm: A novel bio-inspired algorithm for global optimization and engineering design problemsComputer Methods in Applied Mechanics and Engineering10.1016/j.cma.2023.116200415(116200)Online publication date: Oct-2023
    • (2022)Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applicationsComputer Methods in Applied Mechanics and Engineering10.1016/j.cma.2021.114194388(114194)Online publication date: Jan-2022

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