Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of...Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO（ECE-PSO） is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.展开更多
A new class of hybrid particle swarm optimization （PSO） algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly dec...A new class of hybrid particle swarm optimization （PSO） algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique （LDIW） and the mutative scale chaos optimization algorithm （MSCOA） are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.展开更多
This paper presents a new approach based on the particle swarm optimization (PSO) algorithm for solving the drilling path optimization problem belonging to discrete space.Because the standard PSO algorithm is not guar...This paper presents a new approach based on the particle swarm optimization (PSO) algorithm for solving the drilling path optimization problem belonging to discrete space.Because the standard PSO algorithm is not guaranteed to be global convergence or local convergence,based on the mathematical algorithm model,the algorithm is improved by adopting the method of generate the stop evolution particle over again to get the ability of convergence to the global optimization solution.And the operators are improved by establishing the duality transposition method and the handle manner for the elements of the operator,the improved operator can satisfy the need of integer coding in drilling path optimization.The experiment with small node numbers indicates that the improved algorithm has the characteristics of easy realize,fast convergence speed,and better global convergence characteris- tics.hence the new PSO can play a role in solving the problem of drilling path optimization in drilling holes.展开更多
A Particle Swarm Optimizer （PSO） exhibits good performance for optimization problems, although it cannot guarantee convergence to a global, or even local minimum. However, there are some adjustable parameters, and r...A Particle Swarm Optimizer （PSO） exhibits good performance for optimization problems, although it cannot guarantee convergence to a global, or even local minimum. However, there are some adjustable parameters, and restrictive conditions, which can affect the performance of the algorithm. In this paper, the sufficient conditions for the asymptotic stability of an acceleration factor and inertia weight are deduced, the value of the inertia weight w is enhanced to （ 1, 1）. Furthermore a new adaptive PSO algorithm - Acceleration Factor Harmonious PSO （AFHPSO） is proposed, and is proved to be a global search algorithm. AFHPSO is used for the parameter design of a fuzzy controller for a linear motor driving servo system. The performance of the nonlinear model for the servo system demonstrates the effectiveness of the optimized fuzzy controller and AFHPSO.展开更多
BP is a commonly used neural network training method, which has some disadvantages, such as local minima, sensitivity of initial value of weights, total dependence on gradient information. This paper presents some met...BP is a commonly used neural network training method, which has some disadvantages, such as local minima, sensitivity of initial value of weights, total dependence on gradient information. This paper presents some methods to train a neural network, including standard particle swarm optimizer （PSO）, guaranteed convergence particle swarm optimizer （GCPSO）, an improved PSO algorithm, and GCPSO-BP, an algorithm combined GCPSO with BP. The simulation results demonstrate the effectiveness of the three algorithms for neural network training.展开更多
To deal with the demerits of constriction particle swarm optimization(CPSO), such as relapsing into local optima, slow convergence velocity, a modified CPSO algorithm is proposed by improving the velocity update formu...To deal with the demerits of constriction particle swarm optimization(CPSO), such as relapsing into local optima, slow convergence velocity, a modified CPSO algorithm is proposed by improving the velocity update formula of CPSO. The random velocity operator from local optima to global optima is added into the velocity update formula of CPSO to accelerate the convergence speed of the particles to the global optima and reduce the likelihood of being trapped into local optima. Finally the convergence of the algorithm is verified by calculation examples.展开更多
The optimization of a water distribution network （WDN） is a highly nonlinear, multi-modal, and constrained combinatorial problem. Particle swarm opti- mization （PSO） has been shown to be a fast converging algorith...The optimization of a water distribution network （WDN） is a highly nonlinear, multi-modal, and constrained combinatorial problem. Particle swarm opti- mization （PSO） has been shown to be a fast converging algorithm for WDN optimization. An improved estimation of distribution algorithm （EDA） using historic best positions to construct a sample space is hybridized with PSO both in sequential and in parallel to improve population diversity control and avoid premature conver- gence. Two water distribution network benchmark exam- ples from the literature are adopted to evaluate the performance of the proposed hybrid algorithms. The experimental results indicate that the proposed algorithms achieved the literature record minimum （6.081 MS） for the small size Hanoi network. For the large size Balerma network, the parallel hybrid achieved a slightly lower minimum （1.921M） than the current literature reported best minimum （1.923MC）. The average number of evaluations needed to achieve the minimum is one order smaller than most existing algorithms. With a fixed, small number of evaluations, the sequential hybrid outperforms the parallel hybrid showing its capability for fast convergence. The fitness and diversity of the populations were tracked for the proposed algorithms. The track record suggests that constructing an EDA sample space with historic best positions can improve diversity control significantly. Parallel hybridization also helps to improve diversity control yet its effect is relatively less significant.展开更多
In order to overcome the drawbacks of standard particle swarm optimization(PSO)algorithm,such as prematurity and easily trapping in local optimum,a modified PSO algorithm is proposed,in which special techniques,as glo...In order to overcome the drawbacks of standard particle swarm optimization(PSO)algorithm,such as prematurity and easily trapping in local optimum,a modified PSO algorithm is proposed,in which special techniques,as global best perturbation and inertia weight jump threshold are adopted.The convergence speed and accuracy of the algo-rithm are improved.The test by some benchmark problems shows that the proposed algorithm achieves relatively higher performance.Thereafter,the applications of the modified PSO in the radiation pattern synthesis of antenna arrays are presented.展开更多
基金supported by the Aeronautical Science Fund of Shaanxi Province of China(20145596025)
文摘Accelerating the convergence speed and avoiding the local optimal solution are two main goals of particle swarm optimization（PSO）. The very basic PSO model and some variants of PSO do not consider the enhancement of the explorative capability of each particle. Thus these methods have a slow convergence speed and may trap into a local optimal solution. To enhance the explorative capability of particles, a scheme called explorative capability enhancement in PSO（ECE-PSO） is proposed by introducing some virtual particles in random directions with random amplitude. The linearly decreasing method related to the maximum iteration and the nonlinearly decreasing method related to the fitness value of the globally best particle are employed to produce virtual particles. The above two methods are thoroughly compared with four representative advanced PSO variants on eight unimodal and multimodal benchmark problems. Experimental results indicate that the convergence speed and solution quality of ECE-PSO outperform the state-of-the-art PSO variants.
文摘A new class of hybrid particle swarm optimization （PSO） algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique （LDIW） and the mutative scale chaos optimization algorithm （MSCOA） are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.
基金Supported by science and technology development fund of Fuzhou university(2005-XQ-09).
文摘This paper presents a new approach based on the particle swarm optimization (PSO) algorithm for solving the drilling path optimization problem belonging to discrete space.Because the standard PSO algorithm is not guaranteed to be global convergence or local convergence,based on the mathematical algorithm model,the algorithm is improved by adopting the method of generate the stop evolution particle over again to get the ability of convergence to the global optimization solution.And the operators are improved by establishing the duality transposition method and the handle manner for the elements of the operator,the improved operator can satisfy the need of integer coding in drilling path optimization.The experiment with small node numbers indicates that the improved algorithm has the characteristics of easy realize,fast convergence speed,and better global convergence characteris- tics.hence the new PSO can play a role in solving the problem of drilling path optimization in drilling holes.
基金The work was supported by the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutes of MOE, PRC
文摘A Particle Swarm Optimizer （PSO） exhibits good performance for optimization problems, although it cannot guarantee convergence to a global, or even local minimum. However, there are some adjustable parameters, and restrictive conditions, which can affect the performance of the algorithm. In this paper, the sufficient conditions for the asymptotic stability of an acceleration factor and inertia weight are deduced, the value of the inertia weight w is enhanced to （ 1, 1）. Furthermore a new adaptive PSO algorithm - Acceleration Factor Harmonious PSO （AFHPSO） is proposed, and is proved to be a global search algorithm. AFHPSO is used for the parameter design of a fuzzy controller for a linear motor driving servo system. The performance of the nonlinear model for the servo system demonstrates the effectiveness of the optimized fuzzy controller and AFHPSO.
文摘BP is a commonly used neural network training method, which has some disadvantages, such as local minima, sensitivity of initial value of weights, total dependence on gradient information. This paper presents some methods to train a neural network, including standard particle swarm optimizer （PSO）, guaranteed convergence particle swarm optimizer （GCPSO）, an improved PSO algorithm, and GCPSO-BP, an algorithm combined GCPSO with BP. The simulation results demonstrate the effectiveness of the three algorithms for neural network training.
基金supported by the National Natural Science Foundation of China(71171015)the National High Technology Research and Development Program(863 Program)(2012AA112403)
文摘To deal with the demerits of constriction particle swarm optimization(CPSO), such as relapsing into local optima, slow convergence velocity, a modified CPSO algorithm is proposed by improving the velocity update formula of CPSO. The random velocity operator from local optima to global optima is added into the velocity update formula of CPSO to accelerate the convergence speed of the particles to the global optima and reduce the likelihood of being trapped into local optima. Finally the convergence of the algorithm is verified by calculation examples.
基金This work was supported by the National Science Foundation Award 0836046. The opinions expressed in this paper are solely those of the authors, and do not necessarily reflect the views of the funding agency.
文摘The optimization of a water distribution network （WDN） is a highly nonlinear, multi-modal, and constrained combinatorial problem. Particle swarm opti- mization （PSO） has been shown to be a fast converging algorithm for WDN optimization. An improved estimation of distribution algorithm （EDA） using historic best positions to construct a sample space is hybridized with PSO both in sequential and in parallel to improve population diversity control and avoid premature conver- gence. Two water distribution network benchmark exam- ples from the literature are adopted to evaluate the performance of the proposed hybrid algorithms. The experimental results indicate that the proposed algorithms achieved the literature record minimum （6.081 MS） for the small size Hanoi network. For the large size Balerma network, the parallel hybrid achieved a slightly lower minimum （1.921M） than the current literature reported best minimum （1.923MC）. The average number of evaluations needed to achieve the minimum is one order smaller than most existing algorithms. With a fixed, small number of evaluations, the sequential hybrid outperforms the parallel hybrid showing its capability for fast convergence. The fitness and diversity of the populations were tracked for the proposed algorithms. The track record suggests that constructing an EDA sample space with historic best positions can improve diversity control significantly. Parallel hybridization also helps to improve diversity control yet its effect is relatively less significant.
文摘In order to overcome the drawbacks of standard particle swarm optimization(PSO)algorithm,such as prematurity and easily trapping in local optimum,a modified PSO algorithm is proposed,in which special techniques,as global best perturbation and inertia weight jump threshold are adopted.The convergence speed and accuracy of the algo-rithm are improved.The test by some benchmark problems shows that the proposed algorithm achieves relatively higher performance.Thereafter,the applications of the modified PSO in the radiation pattern synthesis of antenna arrays are presented.
