Coalition structure generation with worst case guarantees
Artificial Intelligence
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
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
Multiple UAV Coalitions for a Search and Prosecute Mission
Journal of Intelligent and Robotic Systems
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
Non-additive multi-objective robot coalition formation
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Research towards the coalition formation problem in multi-robot systems has recently gained attention. The main objective of this research is to form the best teams of heterogeneous robots (coalitions) to cater to a given set of tasks. Due to the inherently NP-hard nature of the problem, it is being addressed employing a large number of techniques ranging from heuristics based solutions to evolutionary approaches. The problem becomes more complex when the resource-distribution needed to complete a task is non-additive in nature, i.e., it may not be sufficient to just add the resources to the individual robots forming the coalition to sum up to the resource requirement of the given task but also satisfy the minimum resource distribution on each individual member of the coalition. There may be multiple alternate coalitions for a task, each of which can complete the task but with different efficiency. Traditionally the coalition formation problem has been formulated as a single objective optimization problem wherein the objective is either to maximize the number of the tasks or to maximize the overall system efficiency. In this paper, the coalition formation problem has been modeled as a multi-objective optimization problem where both the number of tasks completed as well as the overall system efficiency are considered simultaneously. Two popular multi-objective approaches are implemented and the results demonstrate their superiority over single objective solutions.