A method combined ant colony algorithm with particle swarm optimization algorithm was designed for solving multi-objective flexible job shop scheduling problem in this paper. In the combined algorithm the start positi...
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ISBN:
(纸本)9783037850992
A method combined ant colony algorithm with particle swarm optimization algorithm was designed for solving multi-objective flexible job shop scheduling problem in this paper. In the combined algorithm the start position of ants was marked by particles optimum position obtained by particle swarm optimization algorithm. Then the traditional ant colony algorithm was improved and was used to search the global optimum scheduling. The combined algorithm was validated by practical instances. The results obtained have shown the proposed approach is feasible and effective for the multi-objective flexible job shop scheduling problem.
This paper presents a Decision Support System (DSS) designed to aid the manager to optimize the scheduling aircrafts (planes) landings at an airport. This problem is one of deciding a landing time for each plane such ...
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ISBN:
(纸本)9783642251252
This paper presents a Decision Support System (DSS) designed to aid the manager to optimize the scheduling aircrafts (planes) landings at an airport. This problem is one of deciding a landing time for each plane such that each plane lands within a predetermined time window and the landings of all successive planes are respected. This DSS is developed using 4D language, it is composed of three components. 1) The Model Subsystem;the particleswarmoptimization metaheuristic allows the optimization model for identification of aircraft that respect the preferences of the decision maker. 2) The Data Subsystem;the DSS uses a knowledge base composed of data publicly available from OR-LIBRARY, involving from 10 to 50 aircrafts (we consider 25 problems in total). 3) The Dialog Subsystem;a visual interactive simulation model will allow the interactive and convivial resolution.
Training fuzzy neural network (FNN) is an optimization task which is desired to find optimal centers of the membership function and weights. Traditional training algorithms have some drawbacks such as getting stuck in...
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ISBN:
(纸本)9781457703218
Training fuzzy neural network (FNN) is an optimization task which is desired to find optimal centers of the membership function and weights. Traditional training algorithms have some drawbacks such as getting stuck in local minima and computational complexity. This work presents FNN trained by artificial bee colony (ABC) optimizationalgorithm which has good exploration and exploitation capabilities. FNN trained by this algorithm is applied to speech recognition system and compares its performance with particleswarmoptimization (PSO) algorithm and back-propagation (BP) algorithm. The experimental results prove that ABC algorithm has better recognition results and convergence speed than FNN trained by BP algorithm and has similar recognition results and convergence speed than FNN trained by PSO.
An improved wavelet neural network algorithm which combines with particleswarmoptimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learnin...
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An improved wavelet neural network algorithm which combines with particleswarmoptimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function.
As a newly discovered evolutionary algorithm, the particle swarm optimization algorithm has been widely used in the synthesis of array antennas, while it is seldom used in the synthesis of nonuniform array antennas. T...
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As a newly discovered evolutionary algorithm, the particle swarm optimization algorithm has been widely used in the synthesis of array antennas, while it is seldom used in the synthesis of nonuniform array antennas. Two different nonuniform array antennas are optimized by binary particleswarmoptimization and real particleswarmoptimization in this article, which depicts the application of particleswarmoptimization in the synthesis of nonuniform array antennas. Lower peak side-lobe level with uniform excitation can be obtained using this method. Meanwhile, the method of minimizing variable-searching space that can improve the efficiency of algorithm is used in particleswarmoptimization. Compared with the standard genetic algorithm and the modified real genetic algorithm, particleswarmoptimization shows high performance in the synthesis of nonuniform array antennas. To demonstrate the universality of the algorithm, a nonuniform circular array and a sparse linear array with a directional element are synthesized as well.
This article presents, asymmetric coplanar strip fed half monopole UWB antenna to cover the ultra-wideband frequency operation. The antenna has staircase shape and very small volume (12 x 21 x 1 mm(3)). The hand-rejec...
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This article presents, asymmetric coplanar strip fed half monopole UWB antenna to cover the ultra-wideband frequency operation. The antenna has staircase shape and very small volume (12 x 21 x 1 mm(3)). The hand-rejection operation achieve at the WLAN (5.15-5.85 GHz) hand by adding spiral defected radiator structure. particle swarm optimization algorithm is employed to achieve ultra-wide band and band-rejection characteristics. The measured frequency response shows an impedance bandwidth of 9 GHz or 120% over 3-12 GHz for VSWR < 2. (C) 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 1510-1513, 2010;Published online in Wiley InterScience (***). DOI 10.1002/mop.25269
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 algorithm. It is well-known that EM approach has a drawback ...
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ISBN:
(纸本)1424400600
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 algorithm. It 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.
This paper presents a novel adaptive multi-objective particle swarm optimization algorithm and with adaptive multi-objective particleswarmalgorithm for solving objective constrained optimization problems, in which P...
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This paper presents a novel adaptive multi-objective particle swarm optimization algorithm and with adaptive multi-objective particleswarmalgorithm for solving objective constrained optimization problems, in which Pareto non-dominated ranking, tournament selection, crowding distance method were introduced, simultaneously the rate of crowding distance changing were integrated into the algorithm. Finally, ten standard functions are used to test the performance of the algorithm, experimental results show that the proposed approach is an efficient and achieve a high-quality performance.
A new modified particle swarm optimization algorithm for linear equation constrained optimization problem was put forward. And the method using this algorithm to train support vector machine was given. In the new algo...
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ISBN:
(纸本)1424403316
A new modified particle swarm optimization algorithm for linear equation constrained optimization problem was put forward. And the method using this algorithm to train support vector machine was given. In the new algorithm, the particle studies not only from itself and the best one but also from other particles in the population with certain probability. This strengthened learning behavior can make the particle to search the whole solution space better. In addition, the mutation for the particle is considered in the new algorithm. The mutation operation is executed when the particleswarm becomes stagnated, which is decided by calculating the population diversity with the formula presented in this paper. For the specific constraints of support vector machine, a new method to initialize the particles in the feasible solution space was provided. The experiments on synthetic and sonar dataset classification show that our algorithm is feasible and robust for support vector machine training.
Coalition is an important way of cooperation for multi-Agent system. To maximize the summation of the coalition values, and to search for an optimized coalition structure in a minimal searching range, a coalition stru...
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ISBN:
(纸本)1424403316
Coalition is an important way of cooperation for multi-Agent system. To maximize the summation of the coalition values, and to search for an optimized coalition structure in a minimal searching range, a coalition structure optimizationalgorithm in multi-Agent systems based on particleswarmalgorithm (PSO) is proposed. A comparison is made between the operation performances of Generic algorithm (GA) and particleswarmoptimization in this matter through simulation experiment. The result of the simulation shows the effectiveness of the PSO algorithm.
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