The problem of scheduling in flowshops with the objective of minimizing makespan, total flowtime and completion time variation is studied in this paper. A simple discrete version of particleswarmoptimization Algorit...
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ISBN:
(纸本)9781424403103
The problem of scheduling in flowshops with the objective of minimizing makespan, total flowtime and completion time variation is studied in this paper. A simple discrete version of particle swarm optimization algorithm (PSO) is proposed for solving the set of benchmark flowshop scheduling problems proposed by Taillard [1]. The obtained results are better when compared with the results published in the literature.
Aim at the characteristics of optimal operation of cascade hydropower stations, a mathematical model that solves multi-stage optimization problem based on particle swarm optimization algorithm is established. The oper...
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ISBN:
(纸本)9781424448135
Aim at the characteristics of optimal operation of cascade hydropower stations, a mathematical model that solves multi-stage optimization problem based on particle swarm optimization algorithm is established. The operating strategy of reservoir was transformed to the variable sequence of reservoir water level array, which is presented by a number string of particle through certain coding form. When the particle meets certain constraints, the performance is evaluated by the predetermined objective function. According to the application research of PSO algorithms and some improve PSO algorithm to the cascade optimal operation in the Yalong River, and the comparative analysis of the performance with GA and ACO algorithm, the advantage of the former one is well presented.
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 r...
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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 particleswarmalgorithm. The method makes the algorithm jump out local optimization and search for the global optimal solution in other areas. To some extent, it overcomes the inherent flaw of PSO that falling into local optimization. Using this method in grid task scheduling can not only generate relevant scheme dynamically, and also make the complete time minimum. The experiment shows that the algorithm achieves a better result in task scheduling.
This paper develops RRF neural network based on particleswarmoptimization (PSO) algorithm. It is composed of a RBF neural network, whose parameters including clustering centers, variances of Radial Basis Function an...
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ISBN:
(纸本)9783642132773
This paper develops RRF neural network based on particleswarmoptimization (PSO) algorithm. It is composed of a RBF neural network, whose parameters including clustering centers, variances of Radial Basis Function and weights are optimized by PSO algorithm. Therefore it has not only simplified the structure of RBF neural network, but also enhanced training speed and mapping accurate. The performance and effectiveness of the proposed method are evaluated by using function simulation and compared with RBF neural network. The result shows that the optimized RBF neural network has significant advantages inspect of fast convergence speed, good generalization ability and not easy to yield minimal local results.
A method combined ant colony algorithm with particle swarm optimization algorithm was designed for solving multi-objective flexible job shop scheduling problem in this *** the combined algorithm the start position of ...
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A method combined ant colony algorithm with particle swarm optimization algorithm was designed for solving multi-objective flexible job shop scheduling problem in this *** the combined algorithm the start position of ants was marked by particles optimum position obtained by particleswarmoptimization *** the traditional ant colony algorithm was improved and was used to search the global optimum *** combined algorithm was validated by practical *** results obtained have shown the proposed approach is feasible and effective for the multi-objective flexible job shop scheduling problem.
Based on the astringency and practicability of particle swarm optimization algorithm (PSO) and T cell's promotions and B cell's restrainability of Immunity particle swarm optimization algorithm(IMPSO) and appl...
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ISBN:
(纸本)9787302139225
Based on the astringency and practicability of particle swarm optimization algorithm (PSO) and T cell's promotions and B cell's restrainability of Immunity particle swarm optimization algorithm(IMPSO) and applied it to PID controllers. It is clear that IMPSO is suitable to Increment PID control according to the simulations and it made the tracking and anti-jamming of IM PID based on IMPSO, IMPSO more effective than those of PID based on PSO and those of IMPID based on Immunity algorithm.
A modified algorithm is proposed according to the multi-objective constrained optimization *** order to let constraint conditions convert to an optimization objective used a transform strategy,which is a satisfactory ...
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A modified algorithm is proposed according to the multi-objective constrained optimization *** order to let constraint conditions convert to an optimization objective used a transform strategy,which is a satisfactory summation function of constraint conditions,to accelerate the convergence rate,a new region changed acceleration mechanism is used,and for shake of improving the local search ability,chaos search technology is *** modified algorithm not only improves the diversity of solution set but also makes the nondominated solutions approach the Pareto set as close as *** last,the algorithm is applied to three classical test functions;the optimization performance of modified algorithm is evaluated and numerical experimental results show the effectiveness of the proposed method.
A modified algorithm is proposed according to the multi-objective constrained optimization problems. In order to let constraint conditions convert to an optimization objective used a transform strategy, which is a sat...
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A modified algorithm is proposed according to the multi-objective constrained optimization problems. In order to let constraint conditions convert to an optimization objective used a transform strategy, which is a satisfactory summation function of constraint conditions, to accelerate the convergence rate, a new region changed acceleration mechanism is used, and for shake of improving the local search ability, chaos search technology is introduced. This modified algorithm not only improves the diversity of solution set but also makes the nondominated solutions approach the Pareto set as close as possible. At last, the algorithm is applied to three classical test functions;the optimization performance of modified algorithm is evaluated and numerical experimental results show the effectiveness of the proposed method.
In order to effectively solve the blending optimization problem in cement production process,an optimization model integrating production indices,the cost and its solution method are ***,the cost and the rate values o...
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In order to effectively solve the blending optimization problem in cement production process,an optimization model integrating production indices,the cost and its solution method are ***,the cost and the rate values of raw materials are incorporated into the optimization model respectively as the objective *** function is used to transform optimization problem with constraints into an unconstrained *** then particle swarm optimization algorithm(PSO) is applied to search the optimum *** the optimization information of swarm becomes stagnant,the "inertia weight" operator,cross and mutation operations based on protection strategy are introduced in the optimization process that make the algorithm to maintain diversity and better convergence *** calculation result shows that the presented method can effectively improve the global search ability and convergence *** optimization result can improve the passing rate of indicators by reducing production cost and the harmful ingredient of the mixed material.
according to the stability of the membership degree and low identification rate in fuzzy inference system, this dissertation proposes the application of adaptive neural network-based fuzzy inference system to engi...
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according to the stability of the membership degree and low identification rate in fuzzy inference system, this dissertation proposes the application of adaptive neural network-based fuzzy inference system to engine error diagnosis. To reduce the impact of excessive parameters on classification accuracy and cost, it also raises an asynchronous parallel particleswarmoptimization method applied to the selection of feature subset. The method use uniform mutation operator, balancing effectively the particles' ability to search globally and to develop. The asynchronous parallel particleswarmoptimizationalgorithm(AP-PSO) that is used to select the feature subset is a potential feature subset that carries the characteristic of firstly initializing every particle as the selection question. Then, the method adopts improved asynchronous parallel particleswarmalgorithm to conduct optimal searching based on the particleswarm that include several feature subsets and evaluate the classification ability (adaptive value) of the feature subset selected by way of Support Vector Machine. Finally, the optimal feature subset is *** verification of the build diagnosis model with data of engine tests, it has been found that the recognition accuracy attain to 98.72％, training error falling to *** experiment indicates that the recognition rate of ANFIS system is significantly better than independent neural network reasoning system, fuzzy inference system.
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