scholarly journals A modified genetic algorithm for task assignment of heterogeneous unmanned aerial vehicle system

2021 ◽  
pp. 002029402110022
Author(s):  
Song Han ◽  
Chenchen Fan ◽  
Xinbin Li ◽  
Xi Luo ◽  
Zhixin Liu
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicle ◽  
Assignment Problem ◽  
Task Assignment ◽  
Good Genes ◽  
Workload Balance ◽  
Aerial Vehicle ◽  
Task Priority ◽  
Task Assignment Problem ◽  
And Task

This study deals with the task assignment problem of heterogeneous unmanned aerial vehicle (UAV) system with the limited resources and task priority constraints. The optimization model which comprehensively considers the resource consumption, task completion effect, and workload balance is formulated. Then, a concept of fuzzy elite degree is proposed to optimize and balance the transmission of good genes and the variation strength of population during the operations of algorithm. Based on the concept, we propose the fuzzy elite strategy genetic algorithm (FESGA) to efficiently solve the complex task assignment problem. In the proposed algorithm, two unlock methods are presented to solve the deadlock problem in the random optimization process; a sudden threat countermeasure (STC) mechanism is presented to help the algorithm quickly respond to the change of task environment caused by sudden threats. The simulation results demonstrate the superiority of the proposed algorithm. Meanwhile, the effectiveness and feasibility of the algorithm in workload balance and task priority constraints are verified.

scholarly journals Toward delay-tolerant multiple-unmanned aerial vehicle scheduling system using Multi-strategy Coevolution algorithm

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881523 ◽  
Author(s):  
Yohanes Khosiawan ◽  
Sebastian Scherer ◽  
Izabela Nielsen
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Unmanned Aerial Vehicle ◽  
Task Assignment ◽  
Computation Time ◽  
Task Sequence ◽  
Swarm Optimization ◽  
Assignment Algorithm ◽  
Aerial Vehicle ◽  
Delay Tolerant

Autonomous bridge inspection operations using unmanned aerial vehicles take multiple task assignments and constraints into account. To efficiently execute the operations, a schedule is required. Generating a cost optimum schedule of multiple-unmanned aerial vehicle operations is known to be Non-deterministic Polynomial-time (NP)-hard. This study approaches such a problem with heuristic-based algorithms to get a high-quality feasible solution in a short computation time. A constructive heuristic called Retractable Chain Task Assignment algorithm is presented to build an evaluable schedule from a task sequence. The task sequence representation is used during the search to perform seamless operations. Retractable Chain Task Assignment algorithm calculates and incorporates slack time to the schedule according to the properties of the task. The slack time acts as a cushion which makes the schedule delay-tolerant. This algorithm is incorporated with a metaheuristic algorithm called Multi-strategy Coevolution to search the solution space. The proposed algorithm is verified through numerical simulations, which take inputs from real flight test data. The obtained solutions are evaluated based on the makespan, battery consumption, computation time, and the robustness level of the schedules. The performance of Multi-strategy Coevolution is compared to Differential Evolution, Particle Swarm Optimization, and Differential Evolution–Fused Particle Swarm Optimization. The simulation results show that Multi-strategy Coevolution gives better objective values than the other algorithms.

Quadratic Convex Reformulation for Solving Task Assignment Problem with Continuous Hopfield Network

2021 ◽  
Author(s):  
Youssef Hami ◽  
Chakir Loqman
Keyword(s):  
Assignment Problem ◽  
Optimal Allocation ◽  
Task Assignment ◽  
Hopfield Network ◽  
Quadratic Program ◽  
Convex Objective Function ◽  
Calculation Results ◽  
Quadratic Convex Reformulation ◽  
Task Assignment Problem ◽  
Theoretical Results

This research is an optimal allocation of tasks to processors in order to minimize the total costs of execution and communication. This problem is called the Task Assignment Problem (TAP) with nonuniform communication costs. To solve the latter, the first step concerns the formulation of the problem by an equivalent zero-one quadratic program with a convex objective function using a convexification technique, based on the smallest eigenvalue. The second step concerns the application of the Continuous Hopfield Network (CHN) to solve the obtained problem. The calculation results are presented for the instances from the literature, compared to solutions obtained both the CPLEX solver and by the heuristic genetic algorithm, and show an improvement in the results obtained by applying only the CHN algorithm. We can see that the proposed approach evaluates the efficiency of the theoretical results and achieves the optimal solutions in a short calculation time.

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