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International Conference on Advanced Design and Manufacturing Engineering (ADME 2011)

作者： Gao, Wei Wuhan Polytech Univ Wuhan 430023 Hubei Peoples R China

ISBN: (纸本)9783037852132

ant colony algorithm is a new bionics optimization algorithm from mimic the swarm intelligence of ant colony behavior. And it is a very good combination optimization method. To extend the ant colony algorithm, and to improve the searching performance, from the connections of continuous optimization and searching process of ant colony algorithm, one new continuous ant colony algorithm is proposed. To verify the new algorithm, the typical functions, such as Schaffer function and "Needle-in-a-haystack" function, are all used. And then, the results of new algorithm are compared with that of immunized evolutionary programming proposed by author.

关键词： ant colony algorithm continuous optimization Combination optimization continuous ant colony algorithm

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Identification of Fuzzy Systems Using a continuous ant colony algorithm

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OPTOELECTRONICS INSTRUMENTATION AND DATA PROCESSING 2012年第1期48卷 54-61页

作者： Khodashinskii, I. A. Dudin, P. A. Tomsk State Univ Control Syst & Radio Elect Pr Lenina 40 Tomsk 634050 Russia

A continuous ant colony algorithm is first proposed to identify fuzzy systems. It is used to construct five fuzzy systems to approximate nonlinear functions of one, two, and three variables. The effect of algorithm parameters on the approximation error is studied. A comparative analysis with other identification algorithms showed an advantage of the proposed algorithm..

关键词： identification fuzzy systems continuous ant colony algorithm

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Study and Application of Displacement Back Analysis Based on CACA-SVM

Study and Application of Displacement Back Analysis Based on...

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3rd International Conference on Civil Engineering, Architecture and Building Materials (CEABM 2013)

作者： Xu, Fei Li, Bin Wang, Ke Hainan Highway Survey & Design Inst Haikou 570206 Hainan Peoples R China

ISBN: (纸本)9783037857755

An new intelligent displacement back analysis, named as CACA-SVM, was proposed based on support vector machine (SVM) and continuous ant colony algorithm (CACA). On the one hand, CACA-SVM used SVM to build the nonlinear mapping relationship between them. On the other hand, CACA-SVM used the global optimization performance of CACA to search the optimal rock mechanics parameters in the global space. The nonlinear mapping relationship built by SVM can fit and forecast the measuring point' displacements under different parameters with high accuracy, avoiding the complex numerical calculation. CACA can prevent object function from trapping in local optimum and improve precision of back analysis. Case study shows that the forecasting trend was in good agreement with the measured trend, which indicated that the model was suitable for solving the geotechnical engineering problem of nonlinearity and uncertainty and could well be applied to displacement back analysis.

关键词： Rock and Soil Mechanics Parameters Support Vector Machine continuous ant colony algorithm Displacement Back Analysis

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New continuous ant colony algorithm

New Continuous Ant Colony Algorithm

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7th World Congress on Intelligent Control and Automation

作者： Gao, Wei Wuhan Polytech Univ Wuhan 430023 Hubei Peoples R China

ISBN: (纸本)9781424421138

ant colony algorithm is a very good combination optimization method from mimic the swarm intelligence of ant colony behaviours. To extend the traditional ant colony algorithm to continuous optimization problems, from the connections of continuous optimization and searching process of ant colony algorithm, here one new continuous ant colony algorithm is proposed. To verify the new algorithm, the typical functions, such as Schaffer function and Percy function, are all used. And then, the results of new continuous ant colony algorithm are compared with that of traditional continuous ant colony algorithm and immunized evolutionary programming proposed by author. The results show that, the convergent speed and computing precision of the new algorithm are all very good.

关键词： ant colony algorithm continuous optimization combination optimization continuous ant colony algorithm

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Study of intelligent optimization methods applied in the fractional Fourier transform

Study of intelligent optimization methods applied in the fra...

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International Conference on Building Materials and Structural Engineering (BMSE 2012)

作者： Wang, HanQing Wang, PingBo Wang, ShuZong Li, MaoLin Navy Univ Engn Wuhan Peoples R China

ISBN: (纸本)9783037853603

In order to overcome the inefficiency shortcoming of traditional step-based searching method for extremum seeking in two-dimensional fractional Fourier domain, some typical intelligent optimization methods such as genetic algorithms, continuous ant colony algorithm, particle swarm optimization and chaos optimization method are introduced and applied successfully in fractional Fourier transform. The performances of the global optimization methods are compared with step-based method based on simulation. Results show that the COA optimization algorithm is much more preferable considering computation efficiency, precision and resolution in all the above mentioned optimization methods.

关键词： the fractional Fourier transform genetic algorithms continuous ant colony algorithm particle swarm optimization chaos optimization algorithm extremum seeking

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Improved chaos optimization method in the fractional Fourier transform

Improved chaos optimization method in the fractional Fourier...

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2nd International Conference on Signal Acquisition and Processing

作者： Wei Hongkai Wang Pingbo Cai Zhiming Fu Yinfeng Navy Univ Engn Wuhan Hubei Peoples R China Shanghai Marine Elect Equipment Res Inst Shanghai Peoples R China

ISBN: (纸本)9780769539607

In order to overcome the inefficiency shortcoming of traditional step-based searching method for extremum seeking in two-dimensional fractional Fourier domain, the chaos optimization method is introduced and applied successfully in fractional Fourier transform. To accelerate the convergence further, two improved chaos optimization methods are proposed. The performances of the proposed optimization methods are verified by comparing with step-based method and other intelligent optimization methods such as genetic algorithms, continuous ant colony algorithm and particle swarm optimization based on simulation. Results show that the second presented chaos optimization algorithm is more preferable considering computation efficiency, precision and resolution.

关键词： fractional Fourier transform genetic algorithms continuous ant colony algorithm particle swarm optimization chaos optimization algorithm extremum seeking

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Oil Production Optimization in Petroleum Reservoirs by ant algorithm

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CHEMICAL PRODUCT AND PROCESS MODELING 2008年第1期3卷

作者： Razavi, Fatemeh Jalali-Farahani, Farhang Univ Tehran Inst Petr Engn Tehran Iran

The objective of the research presented in this paper is to investigate the effectiveness of a new stochastic algorithm to obtain maximum hydrocarbon production rate in petroleum reservoirs. The hydrocarbon production rate is a function of several variables. Some of these variables are production well location, production well flow pressure, injection well location and fluid injection rate. Changing any of the variables will change the hydrocarbon production rate value. By using stochastic optimization techniques, there is no limit to the number of decision variables that can be optimized simultaneously. The presented method in this paper is a new metaheuristic algorithm which enables us to analyze a system of mathematical equations containing a large number of decision variables and determine the optimum values of them that should give the most economic result. This is referred to as the ant colony Optimization algorithm. ant colony Optimization technique leads us to lower computational cost to optimize complicated problems too. Although applied to discrete domains, this algorithm with some modifications has been applied to continuous optimization. Here, a numerical simulation is initially applied for modeling the hydrocarbon reservoir and then the results are analyzed and furthermore optimized to enhance profitability using ant colony Optimization algorithm. The developed code in MATLAB environment, based on ant colony Optimization, is able to estimate production and injection wells location, fluid injection rate and well flow pressure which would result in improved hydrocarbon production. This algorithm is simple to implement and the results of case studies show its ability to provide fast and accurate solutions. Results prove the reliability of algorithm for solving a large class of optimization problems in petroleum engineering.

关键词： continuous ant colony algorithm optimization oil production rate reservoir simulation

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