Stock price prediction is the main concern for financial firms and private investors. In this paper, we proposed a hybrid BP neural network combining adaptive PSO algorithm (HBP-PSO) to predict the stock price. HBP-PSO takes full use of the global searching capability of PSO and the local searching advantages of BP Neural Network. The PSO algorithm is applied for training the connection weights...

UAVs are attracting more and more attentions for their versatilities and low costs. This paper focuses on their security and considers launching jamming attacks on them. We firstly formulate the UAVs jamming problem. Secondly the PSO (Particle Swarm Optimization) algorithm is introduced and new metrics like AJRL (Area for jamming a receiving link) and NJRL (Number of AJRLs) are defined. Then we...

Financial forecasting is an estimation of future financial outcomes for a company, industry, country using historical internal accounting and sales data. We may predict the future outcome of BSE_SENSEX practically by some soft computing techniques and can also optimized using PSO (Particle Swarm Optimization), EA (Evolutionary Algorithm) or DEA (Differential Evolutionary Algorithm) etc. PSO is ...

This paper presents a method for data model partitioning of power distribution network. Modern Distribution Management Systems which utilize multiprocessor systems for efficient processing of large data model are considered. The data model partitioning is carried out for parallelization of analytical power calculations. The proposed algorithms (Particle Swarm Optimization (PSO) and distributed ...

Blind identification of MIMO FIR systems has widely received attentions in various fields of wireless data communications. Here, we use Particle Swarm Optimization (PSO) as the update mechanism of the well-known inverse filtering approach and we show its good performance compared to original method. Specially, the proposed method is shown to be more robust against lower SNR scenarios or in case...

-Aiming at the shortcoming of basic PSO algorithm, that is, easily trapping into local minimum, we propose an advanced PSO algorithm with SA and apply this new algorithm for solving TSP problem. The core of algorithm is based on the PSO algorithm. SA method is used to slow down the degeneration of the PSO swarm and increase the swarm’s diversity. The comparative experiments were made between PS...

This paper investigates the ability of the Particle Swarm Optimization (PSO) method to cope with minimax problems through experiments on well{known test functions. Experimental results indicate that PSO tackles minimax problems e ectively. Moreover, PSO alleviates di culties that might be encountered by gradient{based methods, due to the nature of the minimax objective function, and potentially...

Particle Swarm Optimisation (PSO) is an intelligent search method based on swarm intelligence and has been widely used in many fields. However it is also easily trapped in local optima. In this paper, we propose two hybrid PSO algorithms: one uses a Differential Evolution (DE) operator to replace the standard PSO method for updating a particle’s position; and the other integrates both the DE op...

This paper proposes an improved particle swarm optimization named PSO with Controllable Random Exploration Velocity (PSO-CREV) behaving an additional exploration behavior. Different from other improvements on PSO, the updating principle of PSO-CREV is constructed in terms of stochastic approximation diagram. Hence a stochastic velocity independent on cognitive and social components of PSO can b...

Particle swarm optimization (PSO) is a new evolutionary computation technique. Although PSO algorithm possesses many attractive properties, the methods of selecting inertia weight need to be further investigated. Under this consideration, the inertia weight employing random number uniformly distributed in [0,1] was introduced to improve the performance of PSO algorithm in this work. Three bench...