An innovative hybrid stages particle swarm optimization (HSPSO) learning method, contains fuzzy c-mean (FCM) clustering, particle swarm optimization (PSO) and recursive least-squares, is developed to generate evolutional fuzzy modeling systems to approach three different nonlinear functions. In spite of the adaptive ability of PSO algorithm, its training result is not desirable for the reason o...

Particle swarm optimization (PSO) is an efficient tool for optimization and search problems. However, it is easy to be trapped into local optima due to its information sharing mechanism. Many research works have shown that mutation operators can help PSO prevent premature convergence. In this paper, several mutation operators that are based on the global best particle are investigated and compa...

So far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is Particle Swarm Optimization (PSO). Prior some efforts by applying fuzzy logic for improving defects of PSO such as trapping in local optimums and early convergence has been done. Moreover to overcome the problem of i...

Inspired by social behavior of bird flocking or fish schooling, Eberhart and Kennedy first developed the particle swarm optimization (PSO) algorithm in 1995. PSO, as a branch of evolutionary computation, has been successfully applied in many research and application areas in the past several years, e.g., global optimization, artificial neural network training, and fuzzy system control, etc.. Es...

In this paper, we propose a multiple kernel relevance vector machine (RVM) method based on the adaptive cloud particle swarm optimization (PSO) algorithm to map landslide susceptibility in the low hill area of Sichuan Province, China. In the multi-kernel structure, the kernel selection problem can be solved by adjusting the kernel weight, which determines the single kernel contribution of the f...

Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped ...

A reduce algorithm based on the neighborhood granulation and niche Particle Swarm Optimization (PSO) algorithm is proposed for the reduction of the real decision system with numerical attributes. In this scheme, a rough model is used based on the neighborhood equivalence. The indiscernibility relation is measured by the neighborhood relation, and the universe spaces are approximated by the neig...

An attempt has been made in this paper to solve the non-linear and ill-posed Electrical Impedance Tomography (EIT) inverse problem using clustering-based particle swam optimization (PSO). To enhance optimal search capability in such an ultra high dimension space and improve the quality of the reconstructed image, an adaptive PSO algorithm combined with a modified Newton–Raphson algorithm and a ...

Premature convergence when solving multimodal problems is still the main limitation which affects the performance of the PSO. To avoid of premature, an improved PSO algorithm with an adaptive updating mechanism (IPSO) is proposed in this paper. When the algorithm converges to a local optimum, the updating mechanism begins to work so that the stagnated algorithm obtains energy for optimization. ...

in this paper a novel direct adaptive fuzzy system is proposed to control flexible-joints robot including actuator dynamics. the design includes two interior loops: the inner loop controls the motor position using proposed approach while the outer loop controls the joint angle of the robot using a pid control law. one novelty of this paper is the use of a pso algorithm for optimizing the contro...