Swarm intelligence
Comparing Distributed Reinforcement Learning Approaches to Learn Agent Coordination
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary computing based mobile robot localization
Engineering Applications of Artificial Intelligence
A particle swarm optimization algorithm for the multiple-level warehouse layout design problem
Computers and Industrial Engineering
Scheduling of coupled tasks and one-machine no-wait robotic cells
Computers and Operations Research
Distributed Adaptation in Multi-robot Search Using Particle Swarm Optimization
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Design of a Multi-Robot System Using Mobile Agents with Ant Colony Clustering
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
Expert Systems with Applications: An International Journal
Fitness functions in evolutionary robotics: A survey and analysis
Robotics and Autonomous Systems
Robotics and Computer-Integrated Manufacturing
Navigating a robotic swarm in an uncharted 2D landscape
Applied Soft Computing
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A PSO based optimal switching technique for voltage harmonic reduction of multilevel inverter
Expert Systems with Applications: An International Journal
A new approach for workshop design
Journal of Intelligent Manufacturing
A model to optimize placement operations on dual-head placement machines
Discrete Optimization
Journal of Intelligent Manufacturing
Application of ant colony optimization algorithm in process planning optimization
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing
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This paper deals with a pick and place robotic system design problem. The objective is to present an efficient method which is able to optimize the performances of the robotic system. By defining the suitable combination of scheduling rules, our method allows each robot to perform the assigned pick and place operations in real time in order to maximize the throughput rate. For that, we have developed different resolution methods which define the scheduling rule for each robot in order to seize the products from the first side of the system and to place them on the second side. We suggest three metaheuristics which are the ant colony optimization, the particle swarm optimization and the genetic algorithm. Then, we try to select the best algorithm which is able to get the best solutions with the lowest execution times. This is the main advantage of our methods compared to exact methods. This fact represents a great interest taking in consideration that our methods must respect a strong industrial constraint regarding the functioning of a real industrial robotic system. This constraint states that the answer time to manage the seizing strategies of the robots must be less than 1 second. Numerical results show that the different algorithms perform optimally for the tested instances in a reasonable computational time.