Harrison, Kyle RobertOmbuki-Berman, Beatrice M.Engelbrecht, Andries P.2018-11-262018-10Harrison K.R., Ombuki-Berman B.M., Engelbrecht A.P. (2018) Gaussian-Valued Particle Swarm Optimization. In: Dorigo M., Birattari M., Blum C., Christensen A., Reina A., Trianni V. (eds) Swarm Intelligence. ANTS 2018. Lecture Notes in Computer Science, vol 11172. Springer, Cham,0302-9743 (print)1611-3349 (online)10.1007/978-3-030-00533-7_31http://hdl.handle.net/2263/67321This paper examines the position update equation of the particle swarm optimization (PSO) algorithm, leading to the proposal of a simplified position update based upon a Gaussian distribution. The proposed algorithm, Gaussian-valued particle swarm optimization (GVPSO), generates probabilistic positions by retaining key elements of the canonical update procedure while also removing the need to specify values for the traditional PSO control parameters. Experimental results across a set of 60 benchmark problems indicate that GVPSO outperforms both the standard PSO and the bare bones particle swarm optimization (BBPSO) algorithm, which also employs a Gaussian distribution to generate particle positions.en© Springer Nature Switzerland AG 2018. The original publication is available at : http://link.springer.combookseries/558.Particle swarm optimization (PSO)Gaussian-valued particle swarm optimization (GVPSO)Bare bones particle swarm optimization (BBPSO)Gaussian distributionSwarm intelligenceBench-mark problemsControl parametersParticle positionPosition updatesStandard PSOPostprint Article