This paper proposes the use of a particle filter with embedded particle swarm optimization as an efficient and effective way of dealing with 3d model-based human body tracking. A particle swarm optimization algorithm is utilized in the particle filter to shift the particles toward more promising configurations of the human model. The algorithm is shown to be able of tracking full articulated bo...

How to keep a balance between exploitation and exploration in Particle Swarm Optimization (PSO) for efficiently solving various optimization problems is an important issue. In order to handle premature convergence in PSO search, this paper proposes a novel algorithm, called Particle Swarm Optimization with Diversive Curiosity (PSO/DC), that introduces a mechanism of diversive curiosity into PSO...

In optimizing the particle swarm optimization (PSO) that inevitable existence problem of prematurity and the local convergence, this paper base on this aspects is put forward a kind of modified particle swarm optimization algorithm, take the gradient descent method (BP algorithm) as a particle swarm operator embedded in particle swarm algorithm, and at the same time use to attenuation wall (Dam...

This paper present, a new Velocity Feedback Adaptive Particle Swarm Optimization (VFAPSO) algorithm for Congestion Management (CM) using optimal re-scheduling of both real and reactive power generation with capacitor reactive support. The optimal rescheduling of powers in a pool model is formulated as a constrained nonlinear optimization problem. The paper proposes the application of VFAPSO alg...

in this paper, a novel method is developed for designing the output feedback controller for static synchronous series compensator (sssc). in the proposed method, the problem of selecting the output feedback gains for the sssc controllers is changed into an optimization problem with a time domain-based objective function.then, it is solved by using the particle swarm optimization (pso) algorithm...

in this paper, the gain in ld-celp speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (pso) algorithms to optimize the structure and parameters of neural networks. elman, multi-layer perceptron (mlp) and fuzzy artmap are the candidate neural models. the optimized number of nodes in the first and second hidden layers of el...

estimation of parameters of a hydrologic model is undertaken using a procedure called “calibration” in order to obtain predictions as close as possible to observed values. this study aimed to use the particle swarm optimization (pso) algorithm for automatic calibration of the hec-hms hydrologic model, which includes a library of different event-based models for simulating the rainfall-runoff pr...

Determination of optimum location for drilling a new well not only requires engineering judgments but also consumes excessive computational time. Additionally, availability of many physical constraints such as the well length, trajectory, and completion type and the numerous affecting parameters including, well type, well numbers, well-control variables prompt that the optimization approaches b...

Particle swarm optimization (PSO) has been successfully applied to solve various optimization problems. Recently, a state-based algorithm called multi-state particle swarm optimization (MSPSO) has been proposed to solve discrete combinatorial optimization problems. The algorithm operates based on a simplified mechanism of transition between two states. However, the MSPSO algorithm has to deal w...

Interaction between users in online social networks plays a key role in social network analysis. One on important types of social group is full connected relation between some users, which known as clique structure. Therefore finding a maximum clique is essential for some analysis. In this paper, we proposed a new method using ant colony optimization algorithm and particle swarm optimization al...