Abstract
Particle Swarm Optimization (PSO) is a new nature-inspired evolutionary technique simulated with bird flocking and fish schooling. However, the biological model of standard PSO ignores the different decision process of each bird. In nature, if one bird finds some food, generally, it will continue to fly surrounding this spot to find other food, and vice versa. Inspired by this phenomenon, a new swarm intelligent methodology– perceptive particle swarm optimization is designed, in which each particle can apperceive its current status within the whole swarm, and make a dynamic decision by adjusting its next flying direction. Furthermore, a mutation operator is introduced to avoid unsuitable adjustment. Simulation results show the proposed algorithm is effective and efficiency.
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Cai, X., Cui, Z., Zeng, J., Tan, Y. (2007). Perceptive Particle Swarm Optimization: A New Learning Method from Birds Seeking. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_137
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DOI: https://doi.org/10.1007/978-3-540-73007-1_137
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