Parallel Ant Colonies for Combinatorial Optimization Problems
Proceedings of the 11 IPPS/SPDP'99 Workshops Held in Conjunction with the 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing
Performance of digital pheromones for swarming vehicle control
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms
Computers and Operations Research
Short communication: A modified ant optimization algorithm for path planning of UCAV
Applied Soft Computing
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Planning multiple paths with evolutionary speciation
IEEE Transactions on Evolutionary Computation
Evolutionary algorithm based offline/online path planner for UAV navigation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.00 |
Teams of unmanned combat aerial vehicles (UCAVs) are well suited to perform cooperative mission in hostile environment, and cooperative path planning holds great attention for improving the efficiency of multi-UCAV combating. In this paper, a mathematical formulation for cooperative path planning problem is presented based on the analysis of typical constraints in the scenario. Different from previous studies, the formulation introduces cooperation coefficient to estimate how the UCAV flight paths fulfill the cooperative constraints. Then a coevolutionary multi-ant-colony algorithm is designed and implemented to solve the above-mentioned problem, based on multi-ant-colony algorithm and coevolutionary strategy. The state transition rule and pheromone updating strategy is modified to increase the algorithm performance. Finally, the proposed method is validated to be effective and feasible to solve the cooperative constraints efficiently, and is effective for the multi-UCAV cooperative path planning problem.