Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
Practical genetic algorithms
Computer Vision and Fuzzy-Neural Systems
Computer Vision and Fuzzy-Neural Systems
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Modeling wine preferences by data mining from physicochemical properties
Decision Support Systems
A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control
Expert Systems with Applications: An International Journal
Knowledge acquisition in fuzzy-rule-based systems with particle-swarm optimization
IEEE Transactions on Fuzzy Systems
Adaptive fuzzy approach to function approximation with PSO and RLSE
Expert Systems with Applications: An International Journal
Adaptive fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Review: A parameter selection strategy for particle swarm optimization based on particle positions
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy logic. Simulation results show that the proposed approach improves the performance of PSO. First, benchmark mathematical functions are used to illustrate the feasibility of the proposed approach. Then a set of classification problems are used to show the potential applicability of the fuzzy parameter adaptation of PSO.