scholarly journals Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 734 ◽  
Author(s):  
Hao-Xiang Chen ◽  
Ying Nan ◽  
Yi Yang
Keyword(s):  
Assignment Problem ◽  
Time Window ◽  
Search Algorithm ◽  
Task Assignment ◽  
Multiple Time ◽  
Nsga Ii ◽  
Symbiotic Organisms Search ◽  
Simulation Results ◽  
Symbiotic Organisms ◽  
Task Assignment Problem

This paper considers a reconnaissance task assignment problem for multiple unmanned aerial vehicles (UAVs) with different sensor capacities. A modified Multi-Objective Symbiotic Organisms Search algorithm (MOSOS) is adopted to optimize UAVs’ task sequence. A time-window based task model is built for heterogeneous targets. Then, the basic task assignment problem is formulated as a Multiple Time-Window based Dubins Travelling Salesmen Problem (MTWDTSP). Double-chain encoding rules and several criteria are established for the task assignment problem under logical and physical constraints. Pareto dominance determination and global adaptive scaling factors is introduced to improve the performance of original MOSOS. Numerical simulation and Monte-Carlo simulation results for the task assignment problem are also presented in this paper, whereas comparisons with non-dominated sorting genetic algorithm (NSGA-II) and original MOSOS are made to verify the superiority of the proposed method. The simulation results demonstrate that modified SOS outperforms the original MOSOS and NSGA-II in terms of optimality and efficiency of the assignment results in MTWDTSP.

Solving the task assignment problem using Harmony Search algorithm

2012 ◽  
Vol 4 (3) ◽  
pp. 153-169 ◽  
Author(s):  
Ayed Salman ◽  
Imtiaz Ahmad ◽  
Hanaa AL-Rushood ◽  
Suha Hamdan
Keyword(s):  
Assignment Problem ◽  
Search Algorithm ◽  
Harmony Search ◽  
Task Assignment ◽  
Harmony Search Algorithm ◽  
Task Assignment Problem

scholarly journals On-Demanding Information Acquisition in Multi-UAV-Assisted Sensor Network: A Satisfaction-Driven Perspective

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hua Yang ◽  
Jungang Yang ◽  
Wendong Zhao ◽  
Cuntao Liu
Keyword(s):  
Energy Consumption ◽  
User Satisfaction ◽  
Assignment Problem ◽  
Information Acquisition ◽  
Time Window ◽  
Task Assignment ◽  
Total Energy Consumption ◽  
Time Window Constraints ◽  
Task Assignment Problem ◽  
Multi Uav

When multiple heterogeneous unmanned aerial vehicles (UAVs) provide service for multiple users in sensor networks, users’ diverse priorities and corresponding priority-related satisfaction are rarely concerned in traditional task assignment algorithms. A priority-driven user satisfaction model is proposed, in which a piecewise function considering soft time window and users’ different priority levels is designed to describe the relationship between user priority and user satisfaction. On this basis, the multi-UAV task assignment problem is formulated as a combinatorial optimization problem with multiple constraints, where the objective is maximizing the priority-weighted satisfaction of users while minimizing the total energy consumption of UAVs. A multipopulation-based cooperation genetic algorithm (MPCGA) by adapting the idea of “exploration-exploitation” into traditional genetic algorithms (GAs) is proposed, which can solve the task assignment problem in polynomial time. Simulation results show that compared with the algorithm without considering users’ priority-based satisfaction, users’ weighted satisfaction can be improved by about 47% based on our algorithm in situations where users’ information acquisition is tight time-window constraints. In comparison, UAVs’ energy consumption only increased by about 6%. Besides, compared with traditional GA, our proposed algorithm can also improve users’ weighted satisfaction by about 5% with almost the same energy consumption of UAVs.

A novel global harmony search algorithm for task assignment problem

2010 ◽  
Vol 83 (10) ◽  
pp. 1678-1688 ◽  
Author(s):  
Dexuan Zou ◽  
Liqun Gao ◽  
Steven Li ◽  
Jianhua Wu ◽  
Xin Wang
Keyword(s):  
Assignment Problem ◽  
Search Algorithm ◽  
Harmony Search ◽  
Task Assignment ◽  
Harmony Search Algorithm ◽  
Task Assignment Problem

Actuator Task Assignment Based on Auction Method in Wireless Sensor and Actuator Networks

2013 ◽  
Vol 373-375 ◽  
pp. 306-310 ◽  
Author(s):  
Ping Deng ◽  
Ke Li Zhang
Keyword(s):  
Wireless Sensor Network ◽  
Utility Function ◽  
Sensor Network ◽  
Assignment Problem ◽  
Task Assignment ◽  
Wireless Sensor ◽  
Proper Action ◽  
New Challenges ◽  
Simulation Results ◽  
Task Assignment Problem

A typical Wireless Sensor Network (WSN) only performs the action of sensing the environment, the needs of smart interaction with the environment have led to the emergence of Wireless Sensor and Actuator Network (WSAN). With the presence of actuators, WSAN is heterogeneous, which brings about new challenges that need to be addressed. In this paper, the task assignment of actuators in overlapping area is studied. Firstly, a new utility function is defined, which is the standard to choose the proper action actuators in overlapping area. Then, based on the new utility function, a distributed solution called localized auction method to solve task assignment problem in overlapping area is proposed. Simulation results demonstrate that the proposed auction method can assign tasks to the unique actuator and meet the action completion bound.

scholarly journals EXACT ALGORITHMS FOR A TASK ASSIGNMENT PROBLEM

2009 ◽  
Vol 19 (03) ◽  
pp. 451-465 ◽  
Author(s):  
KAMER KAYA ◽  
BORA UÇAR
Keyword(s):  
Assignment Problem ◽  
Heterogeneous Network ◽  
Task Assignment ◽  
Exact Algorithm ◽  
Exact Algorithms ◽  
Heterogeneous Processors ◽  
Communication Costs ◽  
Wide Range ◽  
Simulation Results ◽  
Task Assignment Problem

We consider the following task assignment problem. Communicating tasks are to be assigned to heterogeneous processors interconnected with a heterogeneous network. The objective is to minimize the total sum of the execution and communication costs. The problem is NP-hard. We present an exact algorithm based on the well-known A* search. We report simulation results over a wide range of parameters where the largest solved instance contains about three hundred tasks to be assigned to eight processors.

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