A novel particle swarm optimization algorithm with adaptive inertia weight

By A. Nickabadi, M. Ebadzadeh, Reza Safabakhsh. Applied Soft Computing, 2011

802 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, background): “Similarity measure (Leu and Yeh, 2012) and success rate (Nickabadi et al., 2011) are two other candidate measures to determine the state of the search; however, there is no concrete evidence (apart from experiments) to show that these strategies are”
Particle swarm optimization algorithm: an overview (2017, methodology): “…there appeared some PSO variants whose inertia weight declined linearly along with iteration times (Shi and Eberhart 1998), adaptive changed (Nickabadi et al. 2011), adjusted by a quadratic function (Tang et al. 2011) and by the population information (Zhan et al. 2009), adjusted based on…”
A competitive mechanism based multi-objective particle swarm optimizer with fast convergence (2018, neutral): “Particle swarm optimization (PSO) [19], as one of the most classical swarm intelligence algorithms, has been widely applied to solve SOPs due to its simple implementation and fast convergence.”