Conceptual Design of Medium Altitude Long Endurance UAV Using Multi Objective Genetic Algorithm

2007 ◽  
Author(s):  
S Rajagopal ◽  
Ranjan Ganguli ◽  
A Pillai ◽  
A Lurdharaj
Keyword(s):  
Genetic Algorithm ◽  
Conceptual Design ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Long Endurance

Conceptual design of UAV using Kriging based multi-objective genetic algorithm

2008 ◽  
Vol 112 (1137) ◽  
pp. 653-662 ◽  
Author(s):  
S. Rajagopal ◽  
R. Ganguli
Keyword(s):  
Genetic Algorithm ◽  
Conceptual Design ◽  
Empirical Relation ◽  
Aircraft Design ◽  
Function Evaluation ◽  
Maximum Speed ◽  
Nsga Ii ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Multidisciplinary Design Optimisation

Abstract This paper highlights unmanned aerial vehicle (UAV) conceptual design using the multi-objective genetic algorithm (MOGA). The design problem is formulated as a multidisciplinary design optimisation (MDO) problem by coupling aerodynamic and structural analysis. The UAV considered in this paper is a low speed, long endurance aircraft. The optimisation problem uses endurance maximization and wing weight minimisation as dual objective functions. In this multi-objective optimisation, aspect ratio, wing loading, taper ratio, thickness-to-chord ratio, loiter velocity and loiter altitude are considered as design variables with stall speed, maximum speed and rate of climb as constraints. The MDO system integrates the aircraft design code, RDS and an empirical relation for objective function evaluation. In this study, the optimisation problem is solved in two approaches. In the first approach, the RDS code is directly integrated in the optimisation loop. In the second approach, Kriging model is employed. The second approach is fast and efficient as the meta-model reduces the time of computation. A relatively new multi-objective evolutionary algorithm named NSGA-II (non-dominated sorting genetic algorithm) is used to capture the full Pareto front for the dual objective problem. As a result of optimisation using multi-objective genetic algorithm, several non-dominated solutions indicating number of useful Pareto optimal designs is identified.

scholarly journals Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hongjing Wei ◽  
Shaobo Li ◽  
Huafeng Quan ◽  
Dacheng Liu ◽  
Shu Rao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Efficient ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm
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