The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Coalition Formation Among Bounded Rational Agents
Coalition Formation Among Bounded Rational Agents
Bayesian Reinforcement Learning for Coalition Formation under Uncertainty
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamic environments
Traderbots: a new paradigm for robust and efficient multirobot coordination in dynamic environments
A robust evolutionary framework for multi-objective optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Quantum evolutionary algorithm for multi-robot coalition formation
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Methods for task allocation via agent coalition formation
Artificial Intelligence
A quantum-inspired ant colony optimization for robot coalition formation
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
CoMutaR: a framework for multi-robot coordination and task allocation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Repeated auctions for robust task execution by a robot team
Robotics and Autonomous Systems
Cooperative control through objective achievement
Robotics and Autonomous Systems
Coalition formation for task allocation: theory and algorithms
Autonomous Agents and Multi-Agent Systems
Autonomous Agents and Multi-Agent Systems
A Generic Framework for Distributed Multirobot Cooperation
Journal of Intelligent and Robotic Systems
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part I
Multi-objective robot coalition formation for non-additive environments
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part I
Multi-robot coalition formation
IEEE Transactions on Robotics
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
IEEE Transactions on Evolutionary Computation
Multi-robot coalition formation in real-time scenarios
Robotics and Autonomous Systems
Social-welfare based task allocation for multi-robot systems with resource constraints
Computers and Industrial Engineering
Considering inter-task resource constraints in task allocation
Autonomous Agents and Multi-Agent Systems
Hi-index | 12.05 |
Manifold increase in the complexity of robotic tasks has mandated the use of robotic teams called coalitions that collaborate to perform complex tasks. In this scenario, the problem of allocating tasks to teams of robots (also known as the coalition formation problem) assumes significance. So far, solutions to this NP-hard problem have focused on optimizing a single utility function such as resource utilization or the number of tasks completed. We have modeled the multi-robot coalition formation problem as a multi-objective optimization problem with conflicting objectives. This paper extends our recent work in multi-objective approaches to robot coalition formation, and proposes the application of the Pareto Archived Evolution Strategy (PAES) algorithm to the coalition formation problem, resulting in more efficient solutions. Simulations were carried out to demonstrate the relative diversity in the solution sets generated by PAES as compared to previously studied methods. Experiments also demonstrate the relative scalability of PAES. Finally, three different selection strategies were implemented to choose solutions from the Pareto optimal set. Impact of the selection strategies on the final coalitions formed has been shown using Player/Stage.