Random Flights for Particle Swarm Optimisers

Alwyn V. Husselmann and Ken A. Hawick


Optimisation, multi-modal functions, flights, walks, swarms


Parametric Optimisation is an important problem that can be tackled with a range of bio-inspired problem space search algorithms. We show how a simplified Particle Swarm Optimiser (PSO) can efficiently exploit advanced space exploration with Lévy flights, Rayleigh flights and Cauchy flights, and we discuss hybrid variations of these. We present implementations of these methods and compare algorithmic convergence on several multi-modal and uni-modal test functions. Random flights considerably enhance the efficient simplified PSO and the Lévy flight gives good balance between local space exploration and local minima avoidance. We discuss computational tradeoffs involved in generating such flights. In summary, these modifications show varying success between themselves for problem solving, but outperforms the uniform random exploration technique in most cases.

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