Based on the combination of the particle swarm algorithm and multiplier penalty function method for the constraint conditions, this paper proposes an improved hybrid particle swarm optimization algorithm which is used to solve nonlinear constraint optimization problems. The algorithm converts nonlinear constraint function into no-constraints nonlinear problems by constructing the multiplier pen...

Particle filter algorithm is a filtering method which uses Monte Carlo idea within the framework of Bayesian estimation theory. It approximates the probability distribution by using particles and discrete random measure which is consisted of their weights, it updates new discrete random measure recursively according to the algorithm. When the sample is large enough, the discrete random measure ...

In this paper, an improved hybrid algorithm combining particle swarm optimization (PSO) with backpropagation algorithm (BP) is proposed to train feedforward neural networks (FNN). PSO is a global search algorithm, but the swarm in PSO is easy to lose its diversity, which results in premature convergence. On the other hand, BP algorithm is a gradient-descent-based method which has good local sea...

Inspired by the model of artificial fish school algorithm, a novel particle swarm optimizer with the increasing visual of artificial fish is presented after introducing the visual of artificial fish into particle swarm optimization. The neighborhood of each particle is dynamically changed through continually expand the visual of each particle. The local optimal strategy and the global optimal s...

Seas and oceans are the most important sources of renewable energy in the world. The main purpose of this paper is to use an appropriate control strategy to improve the performance of point absorbers. In this scheme, considering the high uncertainty in the parameters of the power take-off system in different atmospheric conditions, a new improved black hole algorithm is introduced to tune fuzzy...

This paper presents an optimal design of linear phase digital low pass finite impulse response (FIR) filter using Novel Particle Swarm Optimization (NPSO). NPSO is an improved particle swarm optimization (PSO) that proposes a new definition for the velocity vector and swarm updating and hence the solution quality is improved. The inertia weight has been modified in the PSO to enhance its search...