Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which make<span style="font-family:Verdana;"&g...Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which make<span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> the search move in a more favorable direction. In order to obtain more accurate information about the function shape, this paper propose</span><span style="font-family:Verdana;">s</span><span style="font-family:;" "=""> <span style="font-family:Verdana;">covariance</span><span style="font-family:Verdana;"> matrix learning differential evolution algorithm based on correlation (denoted as RCLDE)</span></span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">to improve the search efficiency of the algorithm. First, a hybrid mutation strategy is designed to balance the diversity and convergence of the population;secondly, the covariance learning matrix is constructed by selecting the individual with the less correlation;then, a comprehensive learning mechanism is comprehensively designed by two covariance matrix learning mechanisms based on the principle of probability. Finally,</span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">the algorithm is tested on the CEC2005, and the experimental results are compared with other effective differential evolution algorithms. The experimental results show that the algorithm proposed in this paper is </span><span style="font-family:Verdana;">an effective algorithm</span><span style="font-family:Verdana;">.</span></span>展开更多
In this paper, inspired by the multiplicative generators of overlap functions, we mainly propose the concepts of multiplicative generator pairs of n-dimensional overlap functions, in order to extend the dimensionality...In this paper, inspired by the multiplicative generators of overlap functions, we mainly propose the concepts of multiplicative generator pairs of n-dimensional overlap functions, in order to extend the dimensionality of overlap functions from 2 to n. We present the condition under which the pair (g, h) can multiplicatively generate an n-dimensional overlap function O<sub>g,h</sub>. we focus on the homogeneity and idempotency property on multiplicatively generated n-dimensional overlap functions.展开更多
In this paper, we introduce the concepts of additive generators and additive generator pair of <em>n</em>-dimensional overlap functions, in order to extend the dimensionality of overlap functions from 2 to...In this paper, we introduce the concepts of additive generators and additive generator pair of <em>n</em>-dimensional overlap functions, in order to extend the dimensionality of overlap functions from 2 to <em>n</em>. We mainly discuss the conditions under which an <em>n</em>-dimensional overlap function can be expressed in terms of its generator pair.展开更多
In this paper, we firstly introduce some new results on overlap functions and <em>n</em>-dimensional overlap functions. On the other hand, in a previous study, Gómez <em>et al</em>. presen...In this paper, we firstly introduce some new results on overlap functions and <em>n</em>-dimensional overlap functions. On the other hand, in a previous study, Gómez <em>et al</em>. presented some open problems. One of these open problems is “to search the construction of <em>n</em>-dimensional overlapping functions based on bi-dimensional overlapping functions”. To answer this open problem, in this paper, we mainly introduce one construction method of <em>n</em>-dimensional overlap functions based on bivariate overlap functions. We mainly use the conjunction operator ∧ to construct <em>n</em>-dimensional overlap functions <img src="Edit_0e82dd84-0f25-4b14-8f26-ae9532b10190.bmp" alt="" /> based on bivariate overlap functions and study their basic properties.展开更多
文摘Differential evolution algorithm based on the covariance matrix learning can adjust the coordinate system according to the characteristics of the population, which make<span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> the search move in a more favorable direction. In order to obtain more accurate information about the function shape, this paper propose</span><span style="font-family:Verdana;">s</span><span style="font-family:;" "=""> <span style="font-family:Verdana;">covariance</span><span style="font-family:Verdana;"> matrix learning differential evolution algorithm based on correlation (denoted as RCLDE)</span></span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">to improve the search efficiency of the algorithm. First, a hybrid mutation strategy is designed to balance the diversity and convergence of the population;secondly, the covariance learning matrix is constructed by selecting the individual with the less correlation;then, a comprehensive learning mechanism is comprehensively designed by two covariance matrix learning mechanisms based on the principle of probability. Finally,</span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">the algorithm is tested on the CEC2005, and the experimental results are compared with other effective differential evolution algorithms. The experimental results show that the algorithm proposed in this paper is </span><span style="font-family:Verdana;">an effective algorithm</span><span style="font-family:Verdana;">.</span></span>
文摘In this paper, inspired by the multiplicative generators of overlap functions, we mainly propose the concepts of multiplicative generator pairs of n-dimensional overlap functions, in order to extend the dimensionality of overlap functions from 2 to n. We present the condition under which the pair (g, h) can multiplicatively generate an n-dimensional overlap function O<sub>g,h</sub>. we focus on the homogeneity and idempotency property on multiplicatively generated n-dimensional overlap functions.
文摘In this paper, we introduce the concepts of additive generators and additive generator pair of <em>n</em>-dimensional overlap functions, in order to extend the dimensionality of overlap functions from 2 to <em>n</em>. We mainly discuss the conditions under which an <em>n</em>-dimensional overlap function can be expressed in terms of its generator pair.
文摘In this paper, we firstly introduce some new results on overlap functions and <em>n</em>-dimensional overlap functions. On the other hand, in a previous study, Gómez <em>et al</em>. presented some open problems. One of these open problems is “to search the construction of <em>n</em>-dimensional overlapping functions based on bi-dimensional overlapping functions”. To answer this open problem, in this paper, we mainly introduce one construction method of <em>n</em>-dimensional overlap functions based on bivariate overlap functions. We mainly use the conjunction operator ∧ to construct <em>n</em>-dimensional overlap functions <img src="Edit_0e82dd84-0f25-4b14-8f26-ae9532b10190.bmp" alt="" /> based on bivariate overlap functions and study their basic properties.
