Abstract
Evaluative techniques offer a tremendous potential for on-line controller design. However, when the optimization space is large and the performance metric is noisy, the time needed to properly evaluate candidate solutions becomes prohibitively large and, as a consequence, the overall adaptation process becomes extremely time consuming. Distributing the adaptation process reduces the required time and increases robustness to failure of individual agents. In this paper, we analyze the role of the four algorithmic parameters that determine the total evaluation time in a distributed implementation of a Particle Swarm Optimization algorithm. For a multi-robot obstacle avoidance case study, we explore in simulation the lower boundaries of these parameters with the goal of reducing the total evaluation time so that it is feasible to implement the adaptation process within a limited amount of time determined by the robots’ energy autonomy. We show that each parameter has a different impact on the final fitness and propose some guidelines for choosing these parameters for real robot implementations.
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References
Akat, S.B., Gazi, V.: Decentralized asynchronous particle swarm optimization. In: IEEE Swarm Intelligence Symposium (2008), doi:10.1109/SIS.2008.4668304
Bratton, D., Kennedy, J.: Defining a Standard for Particle Swarm Optimization. In: IEEE Swarm Intelligence Symposium, pp. 120–127 (2007)
Chang, J., Chu, S., Roddick, J.: A parallel particle swarm optimization algorithm with communication strategies. Journal of Information Science, 809–818 (2005)
Di Mario, E., Mermoud, G., Mastrangeli, M., Martinoli, A.: A trajectory-based calibration method for stochastic motion models. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4341–4347 (2011)
Floreano, D., Mondada, F.: Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 26(3), 396–407 (1996)
Hereford, J., Siebold, M.: Using the particle swarm optimization algorithm for robotic search applications. In: IEEE Swarm Intelligence Symposium, pp. 53–59 (2007)
Jin, Y., Branke, J.: Evolutionary Optimization in Uncertain Environments A Survey. IEEE Transactions on Evolutionary Computation 9(3), 303–317 (2005)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Marques, L., Nunes, U., Almeida, A.T.: Particle swarm-based olfactory guided search. Autonomous Robots 20(3), 277–287 (2006)
Michel, O.: Webots: Professional Mobile Robot Simulation. Advanced Robotic Systems 1(1), 39–42 (2004)
Pan, H., Wang, L., Liu, B.: Particle swarm optimization for function optimization in noisy environment. Applied Mathematics and Computation 181(2), 908–919 (2006)
Parsopoulos, K.E., Vrahatis, M.N.: Particle Swarm Optimizer in Noisy and Continuously Changing Environments. In: Hamza, M.H. (ed.) Artificial Intelligence and Soft Computing, pp. 289–294. IASTED/ACTA Press (2001)
Poli, R.: Analysis of the publications on the applications of particle swarm optimisation. Journal of Artificial Evolution and Applications 2008(2), 1–10 (2008)
Pugh, J., Martinoli, A.: Distributed scalable multi-robot learning using particle swarm optimization. Swarm Intelligence 3(3), 203–222 (2009)
Pugh, J., Zhang, Y., Martinoli, A.: Particle swarm optimization for unsupervised robotic learning. In: IEEE Swarm Intelligence Symposium, pp. 92–99 (2005)
Rada-Vilela, J., Zhang, M., Seah, W.: Random Asynchronous PSO. In: The 5th International Conference on Automation, Robotics and Applications, pp. 220–225 (2011)
Turduev, M., Atas, Y.: Cooperative Chemical Concentration Map Building Using Decentralized Asynchronous Particle Swarm Optimization Based Search by Mobile Robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4175–4180 (2010)
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Di Mario, E., Martinoli, A. (2014). Distributed Particle Swarm Optimization for Limited Time Adaptation in Autonomous Robots. In: Ani Hsieh, M., Chirikjian, G. (eds) Distributed Autonomous Robotic Systems. Springer Tracts in Advanced Robotics, vol 104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55146-8_27
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DOI: https://doi.org/10.1007/978-3-642-55146-8_27
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