Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
A self-generating method for fuzzy system design
Fuzzy Sets and Systems
A GA-based fuzzy adaptive learning control network
Fuzzy Sets and Systems
Future Generation Computer Systems
Swarm intelligence
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Combining Genetic Algorithms and Lyapunov-Based Adaptation for Online Design of Fuzzy Controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Modified PSO Structure Resulting in High Exploration Ability With Convergence Guaranteed
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ant colony optimization for routing and load-balancing: survey and new directions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Genetic algorithm for the design of a class of fuzzy controllers: an alternative approach
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Genetic algorithm-based optimal fuzzy controller design in the linguistic space
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Designing fuzzy-rule-based systems using continuous ant-colony optimization
IEEE Transactions on Fuzzy Systems
Improving fuzzy knowledge integration with particle swarmoptimization
Expert Systems with Applications: An International Journal
Self-adaptive learning based particle swarm optimization
Information Sciences: an International Journal
Multi-basin particle swarm intelligence method for optimal calibration of parametric Lévy models
Expert Systems with Applications: An International Journal
Fuzzy linear regression based on Polynomial Neural Networks
Expert Systems with Applications: An International Journal
Linguistic fuzzy model identification based on PSO with different length of particles
Applied Soft Computing
A hybrid fuzzy rule-based multi-criteria framework for sustainable project portfolio selection
Information Sciences: an International Journal
Hi-index | 12.06 |
This paper proposes a self-generating fuzzy system with a learning ability from a combination of the on-line self-aligning clustering (OSAC) algorithm and ant and particle swarm cooperative optimization (APSCO). The proposed OSAC algorithm not only helps generate rules from on-line training data, but also helps avoid generating highly overlapping fuzzy sets. Once a new rule is generated, APSCO optimizes the corresponding antecedent and consequent parameters. In APSCO, ant colony and particle swarm coexist in a population, and both search for an optimal parameter solution simultaneously in each iteration. Ant paths not only help determine the consequent parameters of generated rules, they also help generate auxiliary particles. Well-performing particles are selected from the auxiliary particles and original particles. And these selected particles cooperate to find a better solution through particle swarm optimization. This paper applies the proposed self-generating fuzzy system to different fuzzy controller design problems, and compares it with other genetic and swarm intelligence algorithms and their hybrids to verify system performance.