Over the recent years, several studies have been carried out by the researchers to describe a general, flexible and powerful design method based on modern heuristic optimisation algorithms for infinite impulse respons...
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Over the recent years, several studies have been carried out by the researchers to describe a general, flexible and powerful design method based on modern heuristic optimisation algorithms for infinite impulse response (IIR) digital filters since these algorithms have the ability of finding global optimal solution in a nonlinear search space. One of the modern heuristic algorithms is the artificial immune algorithm which implements a learning technique inspired by human immune system. However, the immune system has not attracted the same kind of interest from researchers as other heuristic algorithms. In this work, an artificial immune algorithm is described and applied to the design of IIR filters, and its performance is compared to that of genetic and touring ant colony optimisation algorithms. (c) 2005 Elsevier Ltd. All rights reserved.
A new method based on immune algorithm (IA) is presented to solve the scheduling of cogeneration plants in a deregulated market. The objective function includes fuel cost, population cost, and electricity wheeling cos...
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A new method based on immune algorithm (IA) is presented to solve the scheduling of cogeneration plants in a deregulated market. The objective function includes fuel cost, population cost, and electricity wheeling cost, subjective to the use of mixed fuels, operational limits, emissions constraints, and transmission line flow constraints. Enhanced immune algorithm (EIA) is proposed by an improved crossover and mutation mechanism with a competition and auto-adjust scheme to avoid prematurity. Table lists with heuristic rules are also employed in the searching process to enhance the performance. EIA is also compared with the original IA. Test results verify that EIA can offer an efficient way for cogeneration plants to solve the problem of economic dispatch, environmental protection, and electricity wheeling. (C) 2004 Elsevier Ltd. All rights reserved.
Based on the geographic information system (GIS) technology, ArcInfo software was adopted to collect, process andanalyze spatial data of Guangdong Province for an evaluation of soil resource quality. The overlay analy...
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Based on the geographic information system (GIS) technology, ArcInfo software was adopted to collect, process andanalyze spatial data of Guangdong Province for an evaluation of soil resource quality. The overlay analysis method wasused in combining evaluation factors of Guangdong soil resource quality to determine the evaluation units. Because ofits favorable convergent speed and its ability to search solutions, the immune algorithm was applied to the soil resourcequality evaluation model. At the same time, the evaluation results of this newly proposed method were compared to twoother methods: sum of index and fuzzy synthetic. The results indicated that the immune algorithm reflected the actualcondition of soil resource quality more exactly.
The multi-modal function optimization is an important problem with a wide-ranging application. In order to find out all optimal solutions and local optimal solutions as many as possible, an adaptive immune-based optim...
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ISBN:
(纸本)9781424421138
The multi-modal function optimization is an important problem with a wide-ranging application. In order to find out all optimal solutions and local optimal solutions as many as possible, an adaptive immune-based optimization algorithm is proposed based on analyzing the characteristics and disadvantages of clonal selection algorithm, and combining memory cells producing, network suppression and valley searching method. Testing typical multi-modal functions show this algorithm not only has the less computational efforts and the better search capability, but also can realize adaptive searching without any transcendental presumptions.
To improve recognition and generalization capability of back-propagation neural networks (BP-NN), a hidden layer self-organization inspired by immune algorithm called SONIA, is proposed. B cell construction mechanism ...
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To improve recognition and generalization capability of back-propagation neural networks (BP-NN), a hidden layer self-organization inspired by immune algorithm called SONIA, is proposed. B cell construction mechanism of immune algorithm inspires a creation of hidden units having local data recognition ability that improves recognition capability. B cell mutation mechanism inspires a creation of hidden units having diverse data representation characteristics that improves generalization capability. Experiments on a sinusoidal benchmark problem show that the approximation error of the proposed network is 1/17 times lower than that of BP-NN. Experiments on real time-temperature-based food quality prediction data shows that the recognition capability is 18% improved comparing to that of BP-NN. The development of the world first time-temperature-based food quality prediction demonstrates the real applicability of the proposed method in the field of food industry. (C) 2004 Elsevier B.V. All rights reserved.
A hybrid learning method combining immune algorithm and least square method is proposed to design the radial basis function(RBF) networks. The immune algorithm based on information entropy is used to determine the str...
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A hybrid learning method combining immune algorithm and least square method is proposed to design the radial basis function(RBF) networks. The immune algorithm based on information entropy is used to determine the structure and parameters of RBF nonlinear hidden layer, and weights of RBF linear output layer are computed with least square method. By introducing the diversity control and immune memory mechanism, the algorithm improves the efficiency and overcomes the immature problem in genetic algorithm. Computer simulations demonstrate that the RBF networks designed in this method have fast convergence speed with good performances.
The diversity, adaptation and memory of biological immune system attract much attention of researchers. Several optimal algorithms based on immune system have also been proposed up to now. The distance concentra- tion...
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The diversity, adaptation and memory of biological immune system attract much attention of researchers. Several optimal algorithms based on immune system have also been proposed up to now. The distance concentra- tion-based artificial immune algorithm (DCAIA) is proposed to overcome defects of the classical artificial immune al- gorithm (CAIA) in this paper. Compared with genetic algorithm (GA) and CAIA, DCAIA is good for solving the prob- lem of precocity,holding the diversity of antibody, and enhancing convergence rate.
The multi-modal function optimization is an important problem with a wide-ranging application. In order to find out all optimal solutions and local optimal solutions as many as possible, an adaptive immune-based optim...
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The multi-modal function optimization is an important problem with a wide-ranging application. In order to find out all optimal solutions and local optimal solutions as many as possible, an adaptive immune-based optimization algorithm is proposed based on analyzing the characteristics and disadvantages of clonal selection algorithm, and combining memory cells producing, network suppression and valley searching method. Testing typical multi-modal functions show this algorithm not only has the less computational efforts and the better search capability, but also can realize adaptive searching without any transcendental presumptions.
This paper presents an immune PI control that consists of immune optimization algorithm and PI control. Traditional PI control can't quickly match variation of motor variables or load disturbance in permanent magn...
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ISBN:
(纸本)9781424421138
This paper presents an immune PI control that consists of immune optimization algorithm and PI control. Traditional PI control can't quickly match variation of motor variables or load disturbance in permanent magnet synchronous motor since control parameters are discrete and off-line adjusted. Therefore, by designing target function, antigen affinity, antibodies affinity and mutation, the immune algorithm optimizes parameters and memory cells. The tests and the simulation with time-based Parameters demonstrate immune algorithm makes control strategy and has global optimization ability and strong robustness. Based on DSP technology and SVPWM algorithm, the system shows that immune PI control system can obtain stable situation in 50ms, have much improvement in dynamic and static performance.
The clonal selection principle in artificial immune system was introduced. Inspired by the clonal selection process, this paper proposed an immune algorithm to solve function optimization problems and adopted Guassian...
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The clonal selection principle in artificial immune system was introduced. Inspired by the clonal selection process, this paper proposed an immune algorithm to solve function optimization problems and adopted Guassian-like mutation in genetic strategy. The numerical experiment results show that this algorithm has good efficiency, especially in the speed of the convergence, which is greatly better than the traditional Genetic algorithm.
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