Solving Flight Planning Problem for Airborne LiDAR Data Acquisition Using Single and Multi-Objective Genetic Algorithms

Genetic algorithms (GA) are being widely used as an evolutionary optimization technique for solving optimization problems involving non-differentiable objectives and constraints, large dimensional, multi-modal, overly constrained feasible space and plagued with uncertainties and noise. However, to solve different kinds of optimization problems, no single GA works the best and there is a need for customizing a GA by using problem heuristics to solve a specific problem. For the airborne flight planning problem, there is not much prior optimization studies made using any optimization procedure including a GA. In this paper, we make an attempt to devise a customized GA for solving the particular problem to arrive at a reasonably good solution. A step-by-step procedure of the proposed GA is presented and every step of the procedure is explained. Both single and multi-objective versions of the problem are solved for a particular scenario of the flight planning for airborne LiDAR data acquisition problem to demonstrate the use of a GA for such a real-world problem. The deductive approach successfully identifies the appropriate configurations of GA. The paper demonstrates how a systematic procedure of developing a customized optimization procedure for solving a real-world problem involving mixed variables can be devised using an evolutionary optimization procedure.

[1]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[2]  Bertrand Iooss,et al.  Latin hypercube sampling with inequality constraints , 2009, 0909.0329.

[3]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[4]  Stephane Hess,et al.  On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice , 2006 .

[5]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[6]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[7]  Charles Tong,et al.  Refinement strategies for stratified sampling methods , 2006, Reliab. Eng. Syst. Saf..

[8]  Kalyanmoy Deb,et al.  Investigation of Mutation Schemes in Real-Parameter Genetic Algorithms , 2012, SEMCCO.

[9]  K.R. Davey.  Latin Hypercube Sampling and Pattern Search in Magnetic Field Optimization Problems , 2008, IEEE Transactions on Magnetics.

[10]  George Mavrotas,et al.  Effective implementation of the epsilon-constraint method in Multi-Objective Mathematical Programming problems , 2009, Appl. Math. Comput..

[11]  C. Deutsch,et al.  Latin hypercube sampling with multidimensional uniformity , 2012 .

[12]  Kalyanmoy Deb,et al.  Optimization for Engineering Design: Algorithms and Examples , 2004 .

[13]  Jon C. Helton,et al.  Extension of Latin hypercube samples with correlated variables , 2008, Reliab. Eng. Syst. Saf..

[14]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[15]  Kalyanmoy Deb,et al.  A combined genetic adaptive search (GeneAS) for engineering design , 1996 .

[16]  Kalyanmoy Deb,et al.  Analysing mutation schemes for real-parameter genetic algorithms , 2014, Int. J. Artif. Intell. Soft Comput..

[17]  Olov Andersson,et al.  A Gentle Introduction to Machine Learning , 2018 .

[18]  Kalyanmoy Deb,et al.  A population-based algorithm-generator for real-parameter optimization , 2005, Soft Comput..