Fuzzy adaptive particle swarm optimization

By Yuhui Shi, R. Eberhart. Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), 2001

1,213 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/8c125e3360095f3406b86523c0426edd53ccb570

How researchers see this paper

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

In Artificial Intelligence

In Control and Systems Engineering

Citations in the corpus

Fuzzy Adaptive Particle Swarm Optimization for Bidding Strategy in Uniform Price Spot Market (2007, methodology): “For better performance of the PSO search process, the inertia weight should be nonlinearly, dynamically changed to achieve better balance between global and local search abilities [16].”
A review of population-based meta-heuristic algorithm (2013, methodology): “A fuzzy adaptive w was introduced and a random version setting w to was experimented for dynamic system optimization [68, 69].”
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, result): “high, and high frequencies), corresponding to the patterns introduced by Trelea (2003). They showed that the results found by their experimental approach is very similar to what Trelea (2003) has found.”
A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems (2010, methodology): “Later, Shi and Eberhart further designed fuzzy methods to adjust ω nonlinearly [ 3 ].”
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems (2008, methodology): “Other authors have suggested other adjustments to the parameters of the PSO algorithm: adding a random component to the inertia weight [86], [117], [118], applying Fuzzy logic [119], [120], using a secondary PSO to find the optimal parameters of”
A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, methodology): “Different operators like mutation, replication, reproduction, evaluation and selection are utilized in this algorithm (Shi and Eberhart 2001).”
A study of particle swarm optimization particle trajectories (2006, background): “While static inertia values have been used successfully, adaptive inertia values have also shown to lead to convergent behaviour [25] [30] [5] [24] [17] [28].”
Particle swarm optimization versus genetic algorithms for phased array synthesis (2004, methodology): “Many variants of the original particle swarm optimizer have been proposed, such as optimizing multiple subswarms simultaneously [38]–[40], adding negative entropy to stir up the particles [41], sharing discoveries among a local neighborhood of particles [42]–[45], time-varying search goals [46], usi”
Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch (2008, methodology): “Recently fuzzy adaptive PSO [28] has been applied for optimization in power spot price market [29].”
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, methodology): “Using fuzzy sets and membership rules, ω can be dynamically updated as in [17].”
An analysis of particle swarm optimizers (2002, methodology): “Shi and Eberhart recently proposed a technique for adapting the inertia weight dynamically using a fuzzy controller [122, 123].”