Abstract
Particle swarm optimization (PSO) has been used to solve a wide variety of optimization problems. The basic PSO algorithm contains a number of control parameters, including the inertia weight, w, and the acceleration coefficients, c 1 and c 2. The PSO, as an optimization algorithm, is ideally suited to optimize its own parameters. This paper proposes that the control parameters of PSO be optimized in a secondary swarm where each position vector component of each particle contains a prospective PSO control parameter (i.e. w, c 1 and c 2) of the main swarm. This approach relieves the user from specifying appropriate parameters when using PSO. Application of the self-adaptive particle swarm optimizer (SAPSO) to 12 well known test functions shows that SAPSO managed to reach pre-specified values quicker than an adaptive PSO using fitness rank to update the inertia weight.
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Ismail, A., Engelbrecht, A.P. (2012). Self-Adaptive Particle Swarm Optimization. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_23
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DOI: https://doi.org/10.1007/978-3-642-34859-4_23
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