Journal ArticleDOI
An improved evolutionary method with fuzzy logic for combining Particle Swarm Optimization and Genetic Algorithms
Fevrier Valdez,Patricia Melin,Oscar Castillo +2 more
- Vol. 11, Iss: 2, pp 2625-2632
TLDR
A new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using fuzzy logic to integrate the results of both methods and for parameters tuning is described.Abstract:
We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using fuzzy logic to integrate the results of both methods and for parameters tuning. The new optimization method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid approach. Fuzzy logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid FPSO+FGA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The improved hybrid FPSO+FGA method is shown to outperform both individual optimization methods.read more
Citations
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Journal ArticleDOI
A survey on optimization metaheuristics
TL;DR: The components and concepts that are used in various metaheuristics are outlined in order to analyze their similarities and differences and the classification adopted in this paper differentiates between single solution based metaheURistics and population based meta heuristics.
Journal ArticleDOI
A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding
TL;DR: Experiments based on Kapur's entropy indicate that the ABC algorithm can be efficiently used in multilevel thresholding, and CPU time results show that the algorithms are scalable and that the running times of the algorithms seem to grow at a linear rate as the problem size increases.
Journal ArticleDOI
Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic
TL;DR: Simulation results show that the proposed approach to the convergence and diversity of the swarm in PSO using fuzzy logic improves the performance of PSO.
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Optimization of sensor placement for structural health monitoring: a review:
TL;DR: This article reviews the work in the area of optimization of sensor placement and highlights the different pitfalls of the optimization algorithms and the countermeasures different researchers have proposed to overcome the known shortcomings.
Journal ArticleDOI
Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO
TL;DR: In this paper the optimization of type-2 fuzzy inference systems using genetic algorithms and particle swarm optimization is presented and simulation results and a comparative study are presented to illustrate the advantages of the bio-inspired methods.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Proceedings ArticleDOI
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Proceedings ArticleDOI
Flocks, herds and schools: A distributed behavioral model
TL;DR: In this article, an approach based on simulation as an alternative to scripting the paths of each bird individually is explored, with the simulated birds being the particles and the aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course.