Non-additive multi-objective robot coalition formation |
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| Affiliation: | 1. Department of Computer Science, University of Delhi, Delhi 110007, India;2. School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India;1. Nagoya Institute of Technology, Department of Computer Science, Gokisho, Showa, Nagoya, Aichi, 466-8555, Japan;2. University of the Ryukyus, Department of Electrical Engineering, Nakagami, Nishihara, Okinawa, 903-0213, Japan;1. School of Control Science and Engineering, Dalian University of Technology, Dalian City, PR China;2. Department of Electrical Computer Engineering, University of Alberta, Edmonton, AB T6R 2V4, Canada;3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;1. Faculty of Phil. and Arts, University of Kragujevac, Jovana Cvijica bb, 34000 Kragujevac, Serbia;2. Faculty of Economics, University of Nis, Trg kralja Aleksandra Ujedinitelja 11, 18000 Nis, Serbia;3. Faculty of Economics, University of Kragujevac, Djure Pucara Starog 3, 34000 Kragujevac, Serbia;4. Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia;5. College of Applied Mechanical Engineering, Trstenik, Serbia |
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| Abstract: | 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. |
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| Keywords: | Evolutionary algorithms Multi-objective optimization Coalition formation |
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