Synthesis of four-bar Ackermann steering mechanism was considered as an optimization problem for generating the best function between input and output links. The steering mechanism was designed through two heuristic o...
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Synthesis of four-bar Ackermann steering mechanism was considered as an optimization problem for generating the best function between input and output links. The steering mechanism was designed through two heuristic optimization methods, namely, artificialimmunesystem (AIS) algorithm and genetic algorithm (GA). The optimization was implemented using the two methods, length was selected as optimization parameter in the first method, whereas precision point distribution was considered in the second method. Two of the links in the first method had the same length to achieve a symmetric mechanism;one of these lengths was considered as optimization parameter. Five precision points were considered in the precision point distribution method, one of which was in the straight line condition, whereas the others were symmetric. The obtained results showed that the AIS algorithm can generate the closest function to the desired function in the first method. By contrast, GA can generate the closest function to the desired function with the least error in the second method.
In this paper, synthesis of the steering mechanism is optimized using artificialimmunesystem (AIS) algorithm and Genetic algorithm for best function generation. A four-bar mechanism is employed as the Ackermann stee...
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ISBN:
(纸本)9781479906611;9781479906598
In this paper, synthesis of the steering mechanism is optimized using artificialimmunesystem (AIS) algorithm and Genetic algorithm for best function generation. A four-bar mechanism is employed as the Ackermann steering mechanism. For the first time the optimization in this problem is implemented with two methods. In the first method the length are selected as optimization parameter and precision points distribution is considered as optimization parameter in the second method. For the first method two of links have the same length in order to have a symmetric mechanism. For the precision point distribution method, five precision points are considered which one of them is in the straight line condition and the others are symmetric. artificialimmunesystem (AIS) algorithm and GA have been used as appropriate optimization strategies. The obtained result shows that in both methods, the AIS algorithm can produce more accurate function close to the desire function rather than GA. Moreover it has identified that the first method, length as optimization parameter, is more practical because of its ability to satisfy all dimensional constraints.
In this paper, synthesis of the steering mechanism is optimized using artificialimmunesystem (AIS) algorithm and Genetic algorithm for best function generation. A four-bar mechanism is employed as the Ackermann stee...
详细信息
ISBN:
(纸本)9781479906598
In this paper, synthesis of the steering mechanism is optimized using artificialimmunesystem (AIS) algorithm and Genetic algorithm for best function generation. A four-bar mechanism is employed as the Ackermann steering mechanism. For the first time the optimization in this problem is implemented with two methods. In the first method the length are selected as optimization parameter and precision points distribution is considered as optimization parameter in the second method. For the first method two of links have the same length in order to have a symmetric mechanism. For the precision point distribution method, five precision points are considered which one of them is in the straight line condition and the others are symmetric. artificialimmunesystem (AIS) algorithm and GA have been used as appropriate optimization strategies. The obtained result shows that in both methods, the AIS algorithm can produce more accurate function close to the desire function rather than GA. Moreover it has identified that the first method, length as optimization parameter, is more practical because of its ability to satisfy all dimensional constraints.
Dynamic economic dispatch (DED) is an important dynamic problem in power system operation and control. The objective of the problem is to schedule power generation for the online units over a time horizon, satisfying ...
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Dynamic economic dispatch (DED) is an important dynamic problem in power system operation and control. The objective of the problem is to schedule power generation for the online units over a time horizon, satisfying the unit and ramp-rate constraints. In this paper, clonal selection based artificialimmunesystem (AIS) algorithm is used to solve the dynamic economic dispatch problem for generating units with valve-point effect. The feasibility of the proposed method is validated with ten and five unit test systems for a period of 24 h. Results obtained with the proposed approach are compared with other techniques in the literature. The results obtained substantiate the robustness and proficiency of the proposed methodology over other existing techniques in terms of solution quality and computational efficiency. (C) 2011 Elsevier Ltd. All rights reserved.
Case-based reasoning (CBR), a popular problem solving methodology in data mining, solves new problems by analyzing solutions for similar past problems. The many advantages of CBR include rapid learning, the ability to...
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Case-based reasoning (CBR), a popular problem solving methodology in data mining, solves new problems by analyzing solutions for similar past problems. The many advantages of CBR include rapid learning, the ability to use numerous unrestricted domains, minimal knowledge requirements, and effective presentation of knowledge. However, a major difficulty when applying CBR algorithms is selection of appropriate parameter values, features and weight assignment of features, to avoid constructing poor models. Unfortunately, key CBR parameters, beneficial features and the weight assignment of features vary across different problems. This study developed an efficient CBR approach based on artificial immune system algorithm (AISCBR) to increase classification accuracy by improving parameter tuning, feature selection and weight assignment of features. The proposed approach was then compared with those of other studies using the same University of California, Irvine (UCI) data sets. The experimental results showed that the AISCBR can provide better performance than other existing methods, because higher classification accurate rates can be obtained. (C) 2011 Elsevier B. V. All rights reserved.
This paper presents the implementation of distributed controls in a microgrid operation. The approach utilizes the advantages of using Multi Agent systems technology for controlled operation of a microgrid. The auctio...
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ISBN:
(纸本)9781424463336
This paper presents the implementation of distributed controls in a microgrid operation. The approach utilizes the advantages of using Multi Agent systems technology for controlled operation of a microgrid. The auctioneer aims to optimize the operation of the microgrid by optimizing the production of local distributed generators. An artificialimmunesystem based algorithm is applied on a typical study case network assuming realistic market prices for power and distributed generators bids reflecting realistic operational costs. Simulation results clearly indicate that the agent based control framework is effective in coordinating the various distributed energy resources economically.
Based on the clonal selection theory, the main mechanism of immune clone applied in artificial intelligence is analyzed in this paper. A new operator, Adaptive Polyclonal Operator as well as a novel artificialimmune ...
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ISBN:
(纸本)0769519571
Based on the clonal selection theory, the main mechanism of immune clone applied in artificial intelligence is analyzed in this paper. A new operator, Adaptive Polyclonal Operator as well as a novel artificial immune system algorithm, APPA (Adaptive Polyclonal Programming algorithm), is put forward. Compared with some other Evolutionary Programming algorithms (like Breeder Genetic algorithm), APPA, behaving as an evolutionary strategy, is shown to be capable of solving complex machine learning tasks effectively, like Multimodal function Optimization.
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