Research on the Convergence of Fuzzy Genetic Algorithm Based on Rough Classification

Li, Fachao; Yue, Panxiang; Su, Lianqing

doi:10.1007/11881070_105

Cited by 9 publications

(3 citation statements)

References 3 publications

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“…Since 1970s, there have been more than 20 ordering methods, in general, they can be divided into four categories: the method of preference relation, method of fuzzy mean and extended value, method of fuzzy synthetic evaluation and language approximation method. In the sequel, we use the compound quantification strategy of fuzzy information proposed in [9]. [9]).…”

Section: Compound Quantification Description Of Fuzzy Informationmentioning

confidence: 99%

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Study on fuzzy optimization methods based on quasi-linear fuzzy number and genetic algorithm

Liu

Jin

2009

Computers & Mathematics with Applications

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a b s t r a c tFuzzy optimization is a well-known optimization problem in artificial intelligence, system control, manufacturing and management, establishing general and operable fuzzy optimization methods are important in both theory and application. In this paper, starting from the structure of fuzzy information and the mechanism of fuzzy optimization, we propose the concept of quasi-linear fuzzy number, and discuss its approximation properties and the features on arithmetic operations. Further, by distinguishing principal indices and secondary indices, we establish the fuzzy optimization model based on synthesizing effect by combining the compound quantification strategy of fuzzy information, and give a fuzzy optimization method based on principal operations and genetic algorithm (FOM-BPO GA). Finally, we consider the convergence of our algorithm using the theory of Markov chains and analyze its performance through two concrete examples. All these indicate that FOM-BPO GA can effectively merge decision preferences into the optimization process and it also possess better global convergence, so it can be applied to many fuzzy optimization problems.

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Section: Compound Quantification Description Of Fuzzy Informationmentioning

confidence: 99%

“…In the sequel, we use the compound quantification strategy of fuzzy information proposed in [9]. [9]). For an uncertain information A, if the real number a (called the principal value of A) denotes the centralized quantification value in some sense, and the sequence a 1 , a 2 , .…”

Section: Compound Quantification Description Of Fuzzy Informationmentioning

confidence: 99%

Study on fuzzy optimization methods based on quasi-linear fuzzy number and genetic algorithm

Liu

Jin

2009

Computers & Mathematics with Applications

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show abstract

“…Up to now, the usual procedure is to map fuzzy numbers by an appropriate transformation into a real number and thus realize a comparison and ordering of fuzzy numbers. Definition 3.1 (See [32]). For an uncertain information A, if the real number a (called the principal value of A) denotes the centralized quantification value in some sense, and the sequence a 1 , a 2 , .…”

Section: Basic Idea Of Compound Quantificationmentioning

confidence: 99%

Study on fuzzy optimization methods based on principal operation and inequity degree

Jin

2008

Computers & Mathematics with Applications

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a b s t r a c tFuzzy optimization is a well-known optimization problem in artificial intelligence, manufacturing and management, so establishing general and operable fuzzy optimization methods are important in both theory and application. In this paper, by distinguishing principal indices and secondary indices, we give a method for comparing fuzzy information based on synthesizing effect and an operation for achieving fuzzy optimization based on a principal indices transformation. Further, we propose an axiomatic system for fuzzy inequity degree based on the essence of constraint, and give an instructive metric method for fuzzy inequity degree. Then, by combining with genetic algorithm, we give some fuzzy optimization methods based on principal operation and inequity degree (denoted by BPO&ID-FGA, for short). Finally, we consider the convergence of our algorithm using the theory of Markov chains and analyze its performance through two concrete examples. All these indicate that BPO&ID-FGA can effectively merge decision preferences into the optimization process and that it also possesses better global convergence, so it can be applied to many fuzzy optimization problems.

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Fuzzy Genetic Algorithm Based on Principal Operation and Inequity Degree

Jin

2007

Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence

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Research on the Convergence of Fuzzy Genetic Algorithm Based on Rough Classification

Cited by 9 publications

References 3 publications

Study on fuzzy optimization methods based on quasi-linear fuzzy number and genetic algorithm

Study on fuzzy optimization methods based on quasi-linear fuzzy number and genetic algorithm

Study on fuzzy optimization methods based on principal operation and inequity degree

Fuzzy Genetic Algorithm Based on Principal Operation and Inequity Degree

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