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.
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.
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.
A novel particle swarm optimization algorithm-random perturbation particle swarm optimization algorithm(RP-PSO) based on independence of population structure is proposed. To retain diversity of population and avoid be...
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A novel particle swarm optimization algorithm-random perturbation particle swarm optimization algorithm(RP-PSO) based on independence of population structure is proposed. To retain diversity of population and avoid being plunged to local optimum, it initializes the worst individual in population over again, at the same time, the best previous particle of each individual is randomly perturbed after evolutionary computation every time to improve its running efficiency and precision of over all optimization searching. Test results of complex functions demonstrate RAPSO is superior to basic particleswarmoptimization in quality and efficiency.
An effective method of making tradeoff between the optimize precision and optimize speed for load frequency control in the automatic generation control, which can improve the calculating process of particleswarm algo...
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ISBN:
(纸本)1424403316
An effective method of making tradeoff between the optimize precision and optimize speed for load frequency control in the automatic generation control, which can improve the calculating process of particleswarmalgorithm is presented in this paper. This method which is suit for the case that the object to be optimized is complicate can be used to accelerate optimizing process and save calculate time but not influence precision due to the fact that particle swarm optimization algorithm is not sensitive to the number of particles. The method of optimizing PI controller coefficient using promoted particleswarmalgorithm which is used to meet the different performance need in single-area and two-area interconnected power system is proposed respectively. The simulation result shows that the performance is better than the PI controller optimized by genetic algorithm.
This study intends to propose a two-stage clustering algorithm which consists of adaptive resonance theory 2 (ART2) neural network and binary particleswarm K-means optimization (BPSKO) algorithm for grouping the orde...
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This study intends to propose a two-stage clustering algorithm which consists of adaptive resonance theory 2 (ART2) neural network and binary particleswarm K-means optimization (BPSKO) algorithm for grouping the orders together in order to reduce the SMT setup time. The BPSKO algorithm integrates both the particleswarm. optimizationalgorithm and K-means algorithm. Besides, roulette selection operator is applied for avoiding premature convergence. Simulation results using four data sets, Iris, Wine, Vowel, and Glass are very promising. The results for an international industrial personal computer (PG) manufacturer show that the proposed algorithm, ART2+BPSKO, is superior to continuous particle swarm optimization algorithm. Through order clustering, scheduling orders belonging to the same cluster together can, reduce the production time as well as the machine idle time.
particle swarm optimization algorithm is a newly proposed population -based *** efficient in many optimization problems,it may encounter the problem of premature convergence and computational time *** this paper,we at...
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particle swarm optimization algorithm is a newly proposed population -based *** efficient in many optimization problems,it may encounter the problem of premature convergence and computational time *** this paper,we attempt to introduce parallel mechanism into PSO and proposes PPSO(Parallel PSO) *** test the PPSO on four widely known benchmark functions and the experiment results show the efficiency and efficacy of PPSO.
The Improved Vehicle Routing Problem(IVRP) and Location Allocation Problems(LAP) were considered synthetically. And a mathematic model about IVRP has been built *** purpose of this model was to minimize the total cost...
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The Improved Vehicle Routing Problem(IVRP) and Location Allocation Problems(LAP) were considered synthetically. And a mathematic model about IVRP has been built *** purpose of this model was to minimize the total cost by determining the location of the warehouse,allocating appropriate nurnber of vehicles for the selected warehouse and finding the optimal routing for each *** the same time,Improved particle swarm optimization algorithm with Genetic algorithm and Simulated Annealing were applied to solve an example of the *** good convergence state of the global best solution has proved the IVRP model was correct and the algorithm applied to solve IVRP was effective.
The purpose of this paper is to present and evaluate an improved Naive Bayes algorithm for clustering. Many researchers search for parameter values using EM *** is well-known that EM approach has a drawback-local opti...
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The purpose of this paper is to present and evaluate an improved Naive Bayes algorithm for clustering. Many researchers search for parameter values using EM *** is well-known that EM approach has a drawback-local optimal solution, so we propose a novel hybrid algorithm of the Discrete particleswarmoptimization (DPSO) and the EM approach to improve the global search performance. We evaluate this hybrid approach on 4 real-world data sets from UCI repository. In a number of experiments and comparisons,the hybrid DPSO+EM algorithm exhibits a more effective and outperforms the EM approach.
For the main steam temperature system of pulverized coal fired boiler, the modeling precision is not quite satisfactory based on the traditional transfer function. Utilizing the nonlinearity of thermal process, the pa...
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ISBN:
(纸本)9781424467129
For the main steam temperature system of pulverized coal fired boiler, the modeling precision is not quite satisfactory based on the traditional transfer function. Utilizing the nonlinearity of thermal process, the paper proposes the methodology of T-S fuzzy neural network for data fitting. The antecedent parameters are determined by selected centers obtained from simplified subtractive clustering method, and the number of 'If-Then' rules is automatically generated. Afterwards, the improved particle swarm optimization algorithm is proposed to assign the initial consequent parameters of rules which are then fine-tuned by BP algorithm. The simulation results show that the algorithm not only achieves the goal of higher precision, but also exhibits higher generalization ability with respect to the problem of identification and optimization of the main steam temperature system.
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