Introduction to combinatorial theory
Introduction to combinatorial theory
An introduction to genetic algorithms
An introduction to genetic algorithms
A genetic algorithm for the generalised assignment problem
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A genetic algorithm for the vehicle routing problem
Computers and Operations Research
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
A multi-objective-based non-stationary UAV assignment model for constraints handling using PSO
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Multiple UAV teams for multiple tasks
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Multi-UCAV cooperative path planning using improved coevolutionary multi-ant-colony algorithm
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Computers and Operations Research
Info-gap approach to multiagent search under severe uncertainty
IEEE Transactions on Robotics
Using Genetic Algorithms for Tasking Teams of Raven UAVs
Journal of Intelligent and Robotic Systems
A Market-based Solution to the Multiple Traveling Salesmen Problem
Journal of Intelligent and Robotic Systems
Stochastic resource allocation using a predictor-based heuristic for optimization via simulation
Computers and Operations Research
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A problem of assigning cooperating uninhabited aerial vehicles to perform multiple tasks on multiple targets is posed as a new combinatorial optimization problem. A genetic algorithm for solving such a problem is proposed. The algorithm allows us to efficiently solve this NP-hard problem that has prohibitive computational complexity for classical combinatorial optimization methods. It also allows us to take into account the unique requirements of the scenario such as task precedence and coordination, timing constraints, and trajectory limitations. A matrix representation of the genetic algorithm chromosomes simplifies the encoding process and the application of the genetic operators. The performance of the algorithm is compared to that of deterministic branch and bound search and stochastic random search methods. Monte Carlo simulations demonstrate the viability of the genetic algorithm by showing that it consistently and quickly provides good feasible solutions. This makes the real time implementation for high-dimensional problems feasible.