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Design of full band differentiators of integer and non-integer/fractional order using Black Hole Optimization (BHO) algorithm is presented in this paper. The discrete models of the differentiators are obtained without using any s-domain to z-domain generating function. The average performance, pole-zero characteristics, and the error convergence are thoroughly analysed to determine the efficacy of...
Recurrent neural network has been widely used as auto-regressive model for time series. The most commonly used training method for recurrent neural network is back propagation. However, recurrent neural networks trained with back propagation can get trapped at local minima and saddle points. In these cases, auto-regressive models cannot effectively model time series patterns. In order to address these...
This article aims to the problems that the particle swarm optimization (PSO) algorithm has slow convergence and easy to fall into local optimum, provides an improved adaptive particle swarm optimization algorithm based on Levy flight mechanism (LFAPSO). The long jumps of Levy flight will step out of the local optimum in the local search. The convergence speed and accuracy of the LFAPSO algorithm are...
A multi-objective particle swarm algorithm based on the active learning (MOPSAL) approach is proposed that combines a Multi-Objective particle swarm optimization (MOPSO) with an Pareto Active Learning (PAL) approach. In MOPSAL, the candidate solution set is produced by a sampling method based on mutation operator and preselected by the PAL approach. Then, the best Pareto solution from the candidate...
This paper has as a start point the metaheuristic Particle Swarm Optimization (PSO), which has very good abilities to solve many types of optimization problems. As a main contribution, this work proposes an intelligent algorithm derived from PSO. This algorithm has two main characteristics. The first one consists in the use of an improved version of PSO, namely Hybrid Topology Particle Swarm Optimization...
This study rewrote a fractional-order particle swarm optimizer algorithmic equation and used an improved uniform design method (IUDM) to find the best combination for parameters of FPSO. Compared to PSO, FPSO makes a high convergence rate. In the improved FPSO, there are 4 parameters to influence effectiveness. Uniform design is an experimental method and suitable for multiple parameters and multiple...
Quantum-behaved particle swarm optimization (QPSO) is a novel variant of particle swarm optimization (PSO), inspired by quantum mechanics. Compared with traditional PSO, the QPSO algorithm guarantees global convergence and has less number of controlling parameters. However, QPSO is likely to get trapped into a local optimum because of using a single search strategy. This paper proposes a cooperative...
The bare bones particle swarm optimization (BBPSO) is a population-based algorithm. The BBPSO is famous for easy coding and fast applying. A Gaussian distribution is used to control the behavior of the particles. However, every particle learning from a same particle may cause the premature convergence. To solve this problem, a new hierarchical bare bones particle swarm optimization algorithm is proposed...
The dynamic characteristics of a hydraulic turbine governing system is determined by the parameters of the hydraulic turbine governor. There are several drawbacks of the conventional particle swarm algorithm in parameter optimization, such as low speed of convergence, low accuracy and being inclined to result in partial optimization during the process of optimization. This paper introduced concave...
To solve the problem of low recognition rate which is the existing identification methods of partial discharge faults, a new method was designed with wavelet, singular value and improved particle swarm algorithm to optimize the BP neural network. First, using continuous wavelet and singular value decomposition to get the signal characteristic value; then combined with the significance of inertia weight...
Based on the mobile robot path planning problem, on the basis of the improved grid method, this paper proposes an improved ant colony algorithm, the particle swarm optimization algorithm can be incorporated into the ant colony algorithm. Firstly, using the particle swarm optimization algorithm to search for global path roughly. At the same time of search for dynamic pheromone intelligent distribution,...
In this paper, to increase the accuracy and the rate of convergence of the algorithm, by employing orthogonal experiment and mutation operation to the traditional Quantum-behaved Particle Swarm Optimization (QPSO), an improved QPSO (IQPSO) has been presented. The optimization criterion, the ITAE (Integral of Time and Absolute Error) tested that the efficiency of IQPSO is superior to the traditional...
In this paper, a particle swarm optimization method with a new strategy for inertia weight has been considered. The author abandoned the commonly used linear inertia weight and proposed a new dynamic inertia weight based on fitness of the particles. The new weight is a function of the best and the worst fitness of the particles. The considered NIWPSO algorithm was tested on a set of benchmark functions...
This paper investigates horizontal crossover (HC) and stability-based adaptive inertia weight (SAIW) strategies for comprehensive learning particle swarm optimization. HC applies arithmetic crossover on all the dimensions of two different personal best positions. SAIW adaptively adjusts the inertia weight and acceleration coefficient for each particle on each dimension. Experimental results on various...
Particle swarm optimization (PSO) is a state-of-the-art algorithm in meta-heuristic optimization study area. It is a swarm based algorithm that mimic fish or bird's behaviors in the nature. Success rate of convergence in an optimization algorithm depends on control balancing between exploration and exploitation. Inertia weight coefficient parameter controls convergence rate of PSO algorithm. In this...
Particle Swarm Optimization (PSO) is fast and popular algorithm to find the optimum value of non-linear and multi-dimensional function. However, it often easily trapped into local optima because the particles move closer to the best particle quickly. This paper purposes a new algorithm called Multi-Group Particle Swarm Optimization with Random Redistribution (MGRR-PSO) that tried to solve the weakness...
As one widely applied swarm intelligent algorithm, particle swarm optimization (PSO) algorithm has obtained the attention of various scholars with its advantages of easy implementation, high precision and fast convergence. Firstly, aiming to solve the problems that PSO has low searching speed and PSO is easy to fall into local optimal solution especially when dealing with high-dimension model, this...
One of the most classic algorithms for association rules mining is the Apriori algorithm. But it can't satisfy the requirement as the increasing scale of the data. It has some disadvantages such as scanning database too many times, setting support and confidence thresholds artificially. Particle swarm optimization is one of the classic heuristic algorithms and some researchers has used it to association...
Geometric constraint solving is a hot topic in the constraint design research field. Particle swarm optimization (PSO) is a method to solve the optimization problem from the biological population's behavior characteristics. PSO is easy to diverge and fall into the local optimum. There are various kinds of improvements. In addition to improving some performance, the corresponding cost is paid. In this...
The distributed optimal power flow problem is addressed. No assumptions on the problem cost function, and network topology are needed to solve the optimization problem. A distributed particle swarm optimization algorithm is proposed, based on Deb's rule to handle hard constraints. Moreover, the approach enables to treat a class of distributed optimization problems in which the agents share a common...
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