This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the mult...

Aiming at industrial organization multi-objective optimization problem in Equipment Manufacturing Industry, The paper proposes a new type of double layer evolutionary cultural particle swarm optimization algorithm. The algorithm combines the advantages of the particle swarm optimization algorithm and cultural algorithm. It not only revises the problem that the particles are easy to “premature”,...

this paper proposes a novel hybrid algorithm namely apso-bfo which combines merits of bacterial foraging optimization (bfo) algorithm and adaptive particle swarm optimization (apso) algorithm to determine the optimal pid parameters for control of nonlinear systems. to balance between exploration and exploitation, the proposed hybrid algorithm accomplishes global search over the whole search spa...

with the rapid development of the internet, the amount of information and data which are produced, are extremely massive. hence, client will be confused with huge amount of data, and it is difficult to understand which ones are useful. data mining can overcome this problem. while data mining is using on cloud computing, it is reducing time of processing, energy usage and costs. as the speed of ...

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...

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...

The basic Particle Swarm Optimization (PSO) algorithm and its principle have been introduced, the Particle Swarm Optimization has low accelerate speed and can be easy to fall into local extreme value, so the Particle Swarm Optimization based on the improved inertia weight is presented. This method means using nonlinear decreasing weight factor to change the fundamental ways of PSO. To allow ful...

In order to design and optimize high-linearity power amplifier (PA), which with nonlinear and memory effect, it is very important to build power amplifier behavior modeling accurately. This paper proposes a power amplifier behavior modeling based on RBF neural network with improved chaos particle swarm optimization algorithm. To make the particles evenly distribute in the problem search space, ...

in this paper, a new enhanced version of the particle swarm optimization (pso) is presented. an important modification is made by adding probabilistic functions into pso, and it is named probabilistic particle swarm optimization (ppso). since the variation of the velocity of particles in pso constitutes its search engine, it should provide two phases of optimization process which are: explorati...

This paper presents an optimal design of linear phase digital high pass finite impulse response (FIR) filter using Improved Particle Swarm Optimization (IPSO). In the design process, the filter length, pass band and stop band frequencies, feasible pass band and stop band ripple sizes are specified. FIR filter design is a multi-modal optimization problem. An iterative method is introduced to fin...