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Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The performance of micro-population teaching-learning-based optimization(TLBO) algorithm is mainly depended on the population diversity. In order to further improve its performance, this paper proposed a micro-population teaching-learningbased optimization based on gene level diversity(MTLBO-GLD) . In the proposed algorithm, the pop ulation diversity was monitored at the genetic level, and the chaos search and the cosine function were used to improve the population diversity in different evolutionary stages. The proposed algorithm was compared with eight meta-heuristic algorithms ( including four micro-population algorithms and four non-micro-population algorithms) on 13 test functions . The experimental results show that the overall performance of the MTLBO-GLD is significantly better than that of compared algorithms. [ABSTRACT FROM AUTHOR]

To improve the capability of multi-objective differential algorithm, this paper proposed a multi-objective differential evolution algorithm based on self-adaptive strategy(MODE-SS). This algorithm not only used the hyper-volume to evaluate the performance of mutation strategies and selected a suitable mutation strategy automatically, but also employed binomial and simulated binary crossover strategies to balance global and local search capabilities. The results on 10 test functions show that MODE-SS outperforms the other six state-of-the-art multi-objective optimization algorithms. Finally, this paper used MODE-SS to solve the problem of energy consumption optimization of sea-rail intermodal transportation. The results can provide a set of available solutions to decision-makers. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)