Quantity Sensitive Task Allocation Method Based on IWOA in Group Intelligence Perception

JIANG Wei-jin; ZHANG Wan-qing; CHEN Ping-ping; CHEN Jun-peng; SUN Yong-xia; LIU Quan

doi:10.12263/DZXB.20210198

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Quantity Sensitive Task Allocation Method Based on IWOA in Group Intelligence Perception

PAPERS | 更新时间：2025-12-08

- Quantity Sensitive Task Allocation Method Based on IWOA in Group Intelligence Perception
- ACTA ELECTRONICA SINICA Vol. 50, Issue 10, Pages: 2489-2502(2022)
- 作者机构：
  
  1.湖南工商大学前沿交叉学院，湖南长沙 410205
  2.新零售虚拟现实技术湖南省重点实验室，湖南长沙 410205
  3.湖南工商大学计算机学院，湖南长沙 410205
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210198
  CLC： TP393;
- Received：02 February 2021，
  
  Revised：2021-08-04，
  
  Published：25 October 2022
- 稿件说明：
移动端阅览
蒋伟进,张婉清,陈萍萍等.基于IWOA群智感知中数量敏感的任务分配方法[J].电子学报,2022,50(10):2489-2502.

JIANG Wei-jin,ZHANG Wan-qing,CHEN Ping-ping,et al.Quantity Sensitive Task Allocation Method Based on IWOA in Group Intelligence Perception[J].ACTA ELECTRONICA SINICA,2022,50(10):2489-2502.
蒋伟进,张婉清,陈萍萍等.基于IWOA群智感知中数量敏感的任务分配方法[J].电子学报,2022,50(10):2489-2502. DOI： 10.12263/DZXB.20210198.

JIANG Wei-jin,ZHANG Wan-qing,CHEN Ping-ping,et al.Quantity Sensitive Task Allocation Method Based on IWOA in Group Intelligence Perception[J].ACTA ELECTRONICA SINICA,2022,50(10):2489-2502. DOI： 10.12263/DZXB.20210198.

摘要

随着移动终端的大规模普及，群智感知技术以其高效且成本低廉的优势逐渐取代现有的静态传感器，成为一种新兴的数据收集方式.如何在保证质量、降低成本的前提下，把感知任务分配给最佳执行用户以实现用户任务完成数量的最大化，是数量敏感任务分配问题研究的重点.基于此，提出一种结合非线性递减收敛因子、最优局部抖动以及动态位置更新三种操作的改进鲸鱼优化算法（Improved Whale Optimization Algorithm，IWOA），并将其用于求解所提出的任务分配问题.对数量敏感的任务分配问题进行建模，根据用户与任务间的适应程度，定义空间匹配度与技能匹配度.在用户执行任务的过程中考虑到用户的学习能力，引入技能更新机制对用户已有技能进行及时更新，以此提高任务分配的效率.综合考虑预算、用户在线时长以及感知任务完成质量，对最大化任务完成数量的任务分配问题进行合理定义，并从为任务选择最佳执行用户的角度出发，设计一种基于优先级的用户选择策略，以实现在保证感知任务基本完成质量的前提下，降低任务分配的成本.在最优任务分配方案的求解过程中，利用改进算法对每次迭代初始的任务序列进行不断优化，经过有限次迭代即可得到最终结果.将改进算法与其他优化算法在相同环境下进行对比实验，结果表明改进算法在求解任务分配问题时具有更高的性能.

Abstract

With the large-scale popularity of mobile terminals

group intelligence sensing technology gradually replaces the existing static sensors with its advantages of high efficiency and low cost

becoming an emerging data collection method. How to assign perception tasks to the best performing users under the premise of ensuring quality and reducing costs to maximize the number of user tasks completed is the focus of the research on quantity sensitive task allocation. Based on this

a solution based on the improved whale optimization algorithm(IWOA) that combines the three operations of nonlinear decreasing convergence factor

optimal local jitter

and dynamic position update is put forward

which is used to solve the proposed task allocation problem. First

modeling the quantity sensitive task allocation problem

and then defining the spatial matching degree and skill matching degree according to the degree of adaptation between the user and the task. Taking into account the user's learning ability during the user's task execution

the skill update mechanism is introduced to update the user's existing skills in a timely manner

so as to improve task allocation effectiveness. Secondly

comprehensively considering the budget

the user's online time and the perceived task completion quality

and reasonably defining the task allocation problem that maximizes the number of tasks completed. In addition

from the perspective of selecting the best performing user for the task

designing a user selection strategy based on user’s priority to reduce the cost of task allocation while ensuring the quality of the perceived task is basically completed. Then

in the process of solving the optimal task allocation plan

the improved algorithm is used to continuously optimize the initial task sequences of each iteration

and the final result can be obtained after a limited number of iterations. Finally

the improved algorithm is compared with other optimization algorithms in the same environment

and the results show that the improved algorithm has higher performance in solving task allocation problem.

关键词

Keywords

references

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