Multi-agent Path Planning Problem Under a Multi-objective Optimization Framework

Nielsen, Izabela; Bocewicz, Grzegorz; Saha, Subrata

doi:10.1007/978-3-030-53829-3_1

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…The constraint reduced method to solve a MCLP is to "convert one of the two criterion functions, in this case f2(x), into a constraint, which is added to the existing constraint set ." (Arani et al 2020;Mehrez et al 1996), (Hayat et al 2020), (Nielsen et al 2021). The formulation of our toy problem therefore goes from the following objective function and constraint function notation:…”

Section: Methodsmentioning

confidence: 99%

Presenting the Multi-Objective Optimization Model of Search and Rescue Network

Billal¹,

Maleki²

2021

IJSEA

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The Search and Rescue Network (SAR) is a kind of emergency network that pursuit people in need or imminent danger. This paper aims using a priori optimization to demonstrate the optimal assignment of HFDF receivers to the Generalized Search and Rescue (GSAR) network, which is independent of the weighting of the transmitter areas. The mathematical model seeks two objectives, the first one is maximizing the expected number of LOBs for HFDF receivers. The second is providing a fair share number of HFDF receivers allowed to cover the frequency. The result shown the efficiency of presented model ran by CPLEX toolbox of MATLAB 2020 software.

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Section: Methodsmentioning

confidence: 99%

Presenting the Multi-Objective Optimization Model of Search and Rescue Network

Billal¹,

Maleki²

2021

IJSEA

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“…In the present formulation, we ignore the effect of the kinematics of the UAV. We assume that a team of homogeneous UAVs is searching stationary targets in a pre-defined search region [60].…”

Section: Mathematical Modelmentioning

confidence: 99%

UAVs Path Planning under a Bi-Objective Optimization Framework for Smart Cities

et al. 2021

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Unmanned aerial vehicles (UAVs) have been used extensively for search and rescue operations, surveillance, disaster monitoring, attacking terrorists, etc. due to their growing advantages of low-cost, high maneuverability, and easy deployability. This study proposes a mixed-integer programming model under a multi-objective optimization framework to design trajectories that enable a set of UAVs to execute surveillance tasks. The first objective maximizes the cumulative probability of target detection to aim for mission planning success. The second objective ensures minimization of cumulative path length to provide a higher resource utilization goal. A two-step variable neighborhood search (VNS) algorithm is offered, which addresses the combinatorial optimization issue for determining the near-optimal sequence for cell visiting to reach the target. Numerical experiments and simulation results are evaluated in numerous benchmark instances. Results demonstrate that the proposed approach can favorably support practical deployability purposes.

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“…The preferences of the DM determine how the different objectives are compared. In a safety-critical environment, certain objectives are of much higher priority than others [78].…”

Section: Multi-objectivity (If Not Many-objectivity)mentioning

confidence: 99%

Multi-Criteria Decision Making in Complex Decision Environments

Vasegaard

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Multi-agent Path Planning Problem Under a Multi-objective Optimization Framework

Cited by 5 publications

References 32 publications

Presenting the Multi-Objective Optimization Model of Search and Rescue Network

Presenting the Multi-Objective Optimization Model of Search and Rescue Network

UAVs Path Planning under a Bi-Objective Optimization Framework for Smart Cities

Multi-Criteria Decision Making in Complex Decision Environments

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