Mobile Crowd-sensing Multi-task Scheduling Based On Multi-objective Hybrid Frog Leaping Algorith

With the continuous recurrence of the epidemic,a variety of domestic industries have been seriously affected,the employment situation is grim,and social employment pressure is increasing.Mobile crowdsensing can provide a large number of flexible employment positions for the society.However,task scheduling in Mobile crowdsensing determines the interests of many parties,so it is necessary to plan the task scheduling scheme reasonably.The scheduling scheme not only meets the requirements of data reliability,limited energy of users and limited power of users’ equipment,but also increases the income of flexible employees and ensures the completion of tasks on time.As a meta-heuristic optimization algorithm,shuffled frog leaping algorithm has simple concept and strong solving ability,which is suitable for solving NP-hard problems such as task scheduling.Based on the above background,this paper studies the mobile crowdsensing multi-task scheduling based on multi-objective shuffled frog leaping algorithm.The research contents are as follows:Firstly,a three-stage shuffled frog leaping framework is proposed for solving multiobjective combinatorial optimization problems.In this framework,the evolutionary process of population can be divided into three stages: fast convergence,exploring and extending,and extremum mining.For different modules in different stages,different strategies are adopted to improve the solving performance of the framework.The classical multi-objective knapsack problem is used as the test problem,and the discrete leaping rule,greedy generation strategy and relaxed constraint repair operator are designed.Compared with six existing algorithms,the results show that the proposed framework has good performance,and the hybrid leapfrog algorithm designed based on this framework has better convergence and distribution.Secondly,a constrained multi-objective optimization model of variable speed multi-task allocation was established,with user compensation and task completion time as the optimization objectives.A three-stage multi-objective shuffled frog leaping algorithm is proposed.In this paper,an objective anchored hybrid initialization operator based on heuristic information,a region mining strategy for the archive individuals,an improved discrete leaping rule and a constraint handling operator are introduced.The results show that the proposed algorithm has better convergence and distribution,and can find a better Pareto optimal assignment scheme,so as to provide an effective auxiliary decision for the platform to carry out reasonable task assignment.Finally,a many objective optimization model of mobile crowdsensing variable speed multi-task allocation was established considering the power of user equipment.On the basis of the second part,the model adds two optimization objectives of the remaining battery and platform revenue.Based on the second part of the algorithm,a three-stage many objective shuffled frog leaping algorithm is proposed,and a pair matrix of individual relationship,a surrogate based comparison operator.In the simulation experiment,the improved strategy can make the algorithm search for the scheduling scheme with good diversity and convergence.