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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...
In recent years, with the high frequency of the infectious diseases outbreak, the prediction of the infectious diseases has become more and more important, so effective prediction of the infectious diseases can safeguard social stability and promote national economic prosperity. In order to improve the predictive accuracy of infectious diseases, the weight and threshold of BP neural network was optimized...
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...
The particle swarm optimization algorithm is improved by introducing the immune selection, adaptive propagation, multi-population evolution. An improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed in this paper. The performance of several algorithms has been compared by a classic example of traffic network...
An improved multi-objective particle swarm optimization (IMOPSO) is presented because of the different demand for decision and state variables in engineering optimizations. IMOPSO adopts a new method of dynamic change about acceleration coefficients based on sine transform to improve the ability of global search in early period and the local search ability in the last runs of the algorithm. To expand...
In cloud computing environment, there is a large quantity of submitted tasks by users. How to schedule these massive tasks efficiently and reasonably becomes a serious challenge. This paper proposes a Chaotic Particle Swarm Optimization algorithm (CPSO) to overcome the problems of Standard Particle Swarm algorithm such as premature convergence and low accuracy. Firstly, in initial process, chaotic...
The present paper considers convergence characteristics of the particle swarm algorithm and its modification - the hybrid PSO-GS algorithm obtained under combination of the PSO algorithm and Grid Search algorithm. Comparison of quality indices of the particle swarm algorithm and the steepest descent algorithm has been carried out for evaluation of advantages of the PSO algorithm in comparison with...
A novel Quantum-behaved Particle Swarm Optimization algorithm with probability (P-QPSO) is introduced to improve the global convergence property of QPSO. In the proposed algorithm, all the particles keep the original evolution with large probability, and do not update the position of particles with small probability, and re-initialize the position of particles with small probability. Seven benchmark...
Particle swarm optimization algorithm is a simple and effective modern optimization algorithm, but it has the problem of being prone to premature and its convergence rate is slow. A new improved PSO algorithm is hence proposed. In the iteration of the proposed algorithm, the particles are distinguished to be active or stable according to their velocity information. For the active particles, to maintain...
In this paper, the particle swarm optimization algorithm (PSO) for reservoir optimal operation is studied. A new algorithm which is suitable for reservoir optimal operation called multiple groups of gradient particle swarm optimization algorithm (MGPSO) is proposed to avoid the shortcomings of PSO including premature convergence, poor search accuracy and easily falling into local optimal solution...
In this paper, based on Particle Swarm Optimization (PSO) algorithm to observe the different optimization results by changing the objective function. By comparing indicators of various types of objective function, clearly showing its intuitive respective advantages and disadvantages. Herein we can derived from the comprehensive objective function is a relatively good target function, stability, accuracy...
By introducing the fractional-order difference into the updating formulas of the velocity and position, fractional-order particle swarm optimization algorithm is proposed. The effects on the convergence rate and accuracy are analyzed, by introducing fractional-orders in the updating formulas for the velocity and position. Moreover, the linear increasing methods to adjust the fractional-orders are...
Computing grids utilize Internet or special networks to access computing resources which are geographically widespread, in order to solve complex problems more effectively. Task scheduling in grid plays an important role in grid system. This paper introduces mutation into particle swarm algorithm. The method makes the algorithm jump out local optimization and search for the global optimal solution...
Particle swarm optimization (PSO) is a new stochastic optimization technique based on swarm intelligence. In this paper, we introduce the basic principles of PSO firstly. Then, the research progress on PSO algorithm is summarized in several fields, such as parameter selection and design, population topology, hybrid PSO algorithm etc. Finally, some vital applications and aspects that may be conducted...
In the light of the characteristics of the reservoir flood optimal operation, such as multi-restriction, multi-dimension, non-linearity and difficult algorithms, the catfish effect mechanism is introduced into the particle swarm optimization and named catfish effect particle swarm optimization. The arithmetic introduced catfish particles through the startup device of catfish and adjusted the flying...
Low convergence accuracy and the acceleration coefficient setting problem have always been the difficult and hot research points of particle swarm optimization algorithm. This paper introduces a composite particle swarm optimization CPSO based on the adaptive PSO and adaptive GA and applies CPSO in the BP network training of turbo-pump fault diagnosis. In addition, the classical test function Rastrigrin...
Particle Swarm Optimization (PSO) is a new random computational method for tackling optimization functions. However, it is easily trapped into the local optimum when solving the complexity and high-dimensional problems, which makes the performance of PSO greatly reduced. To overcome this shortcoming, the paper proposes an Improved Particle Swarm Optimization (IPSO), by adding the third particle of...
In this paper, an efficient time domain scheme for echo cancellation of speech signals in acoustic environment is proposed. The task of echo cancellation is commonly handled by using the state-of-the-art adaptive filters, which may lack the flexibility of controlling the number of iterations, convergence rate and the range of variation of filter coefficients. In order to tackle these problems, unlike...
Based on the analysis of inertia weight of the standard PSO, a PSO method is described with self-adaptive stochastic inertia weight based on diversity of individual location and fitness value. Position and fitness value correspond to the axis, based on the difference of location and fitness value from the generation and the current generation to construct a right triangle. It is to modify the inertia...
We transform the geometric constraint solving into the numerical optimization solving. A new hybrid algorithm is proposed which combines the merits of global search of the Particle Swarm Optimization Algorithm (PSO) and self organized capacity of ant algorithm. This algorithm uses PSO to search the area where the best solution may exist in the whole space, and then performs fine searching. When the...
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