Ethnic Group Evolution Algorithm

Chen, Hao; Du-wu, Cui; Cui, Ying-An; Tao, Yong-Qin; Liang, Kun

doi:10.3724/sp.j.1001.2010.03484

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…The objective function is assumed to be f (X) = x 2 1 + x 2 2 + x 2 3 , and number i solution of g generation is X g,i = (0.5, 0.5, 0.5), at this time, the objective function f(X g, j ) = 0.75 and the objective function value at this point in the first iteration. When the algorithm is iterative, it is assumed that the whole (X g,i ) is updated to be X g+1, i = (0, 1, −1) by using formula (6) first, then the objective function is evaluated according to the objective function f(X g+1, j ) Value = 2. Since the solution f(X g+1, j ) = 2 > f(X g,i ) of the current generation cannot be improved, the algorithm will give up the new solution and preserve the solution of the current generation.…”

Section: Based On the Greedy Dimension-by-dimension Update Evaluationmentioning

confidence: 99%

“…In the works of Kennedy and Eberhart and Dorigo et al and other groups, intelligent algorithms have been proposed by using the self‐organizing behavior of social living beings such as ants and birds since the 20th century and have been successfully used to solve a large number of practical problems . In recent years, derived from inspiration of the biological system, a number of novel bionic optimization algorithms, such as colony optimization algorithm, optimization algorithm, and self‐balancing robot multi‐dimensional decision‐making assessment (Woodpecker search, SRMDE) algorithm, have emerged.…”

mentioning

confidence: 99%

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Self‐balancing robot bionic intelligence multi‐dimensional decision‐making evaluation algorithm

Ding

Chen

et al. 2018

Concurrency and Computation

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Summary The self‐balancing robot multi‐dimensional decision‐making evaluation (SRMDE) algorithm is a new bionic intelligent algorithm, which adopts an overall updating evaluation strategy for the solution. For solving the multi‐dimensional function optimization problem, due to the mutual interference between the various dimensions, the convergence rate and the quality of the solution will be deteriorated by using the overall update evaluation strategy. In order to compensate for this defect, a multi‐dimensional decision‐making algorithm for self‐balancing robot based on bionic intelligence is proposed. In the iterative process of the improved algorithm, updating evaluation strategy by the dimensionality is adopted for the solution. It combines the updated value of each dimension with the value of other dimensions to form a new solution and accept the update value using greedy way this can improve the solution quality. The results show that the improved strategy can effectively improve the convergence rate of the SRMDE algorithm, and the quality of the solution is improved. Compared with the relative improved self‐balancing robot multi‐dimensional decision‐making algorithm and other evolutionary algorithms, the improved algorithm is competitive in solving the continuous function optimization problem.

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Section: Based On the Greedy Dimension-by-dimension Update Evaluationmentioning

confidence: 99%

mentioning

confidence: 99%

Self‐balancing robot bionic intelligence multi‐dimensional decision‐making evaluation algorithm

Ding

Chen

et al. 2018

Concurrency and Computation

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“…Using block sampling technology can realize quick image division. Using the crossover and mutation feature of genetic algorithm can reconstruct image [6,7]. First determining the image pixels set, pixel can be statistics to set boundaries.…”

Section: Block Sampling Genetic Algorithm Design Of Track and Field I...mentioning

confidence: 99%

Track and Field Image Segmentation Technology of Automatic Multi-Threshold Block Sampling Based on Matlab

2014

AMR

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Based on block sampling and genetic algorithm we improve the image segmentation technology, and obtain the new image processing method based on image pixel block cross reconstruction, and apply this algorithm to the block and the reconstruction calculation of track and field image. In order to verify the effectiveness and reliability of block sampling and genetic algorithm, we use MATLAB to test the algorithm. Through calculation, the number of 2D block is 20, the minimum number of 3D pixel array is 1259, and the calculation residual reaches the highest, with 0.0122. When the number of 2D block is 40, the maximum number of 3D pixel array is 3875, and the calculation residual reaches the lowest, with 0.0013. Therefore, according to the needs of computer hardware conditions, we can increase the 2D block number, improve the segmentation number of 3D pixel array for track and field image, which can reduce the residual and improve the precision of calculation.

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RETRACTED ARTICLE: A self-adaptive genetic algorithm with improved mutation mode based on measurement of population diversity

Sun

2018

Neural Comput & Applic

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Ethnic Group Evolution Algorithm

Cited by 6 publications

References 15 publications

Self‐balancing robot bionic intelligence multi‐dimensional decision‐making evaluation algorithm

Self‐balancing robot bionic intelligence multi‐dimensional decision‐making evaluation algorithm

Track and Field Image Segmentation Technology of Automatic Multi-Threshold Block Sampling Based on Matlab

RETRACTED ARTICLE: A self-adaptive genetic algorithm with improved mutation mode based on measurement of population diversity

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