APA 6th Edition Xie, A. (2018). New intelligent optimization framework. Automatika, 59 (2), 231-253. https://doi.org/10.1080/00051144.2018.1465680
MLA 8th Edition Xie, As. "New intelligent optimization framework." Automatika, vol. 59, br. 2, 2018, str. 231-253. https://doi.org/10.1080/00051144.2018.1465680. Citirano 31.10.2020.
Chicago 17th Edition Xie, As. "New intelligent optimization framework." Automatika 59, br. 2 (2018): 231-253. https://doi.org/10.1080/00051144.2018.1465680
Harvard Xie, A. (2018). 'New intelligent optimization framework', Automatika, 59(2), str. 231-253. https://doi.org/10.1080/00051144.2018.1465680
Vancouver Xie A. New intelligent optimization framework. Automatika [Internet]. 2018 [pristupljeno 31.10.2020.];59(2):231-253. https://doi.org/10.1080/00051144.2018.1465680
IEEE A. Xie, "New intelligent optimization framework", Automatika, vol.59, br. 2, str. 231-253, 2018. [Online]. https://doi.org/10.1080/00051144.2018.1465680
Sažetak Generally, the “intelligence” of the intelligent optimization algorithms is mainly dependent on the probability and operational rules. Thus there are always some probability equations or mathematical formulations that need to be updated. This paper proposes an algorithm model that
needs no probability tuning. The algorithm designed according to the guiding principles and specific methods of benchmarking proposed in this paper is able to achieve the synergistic coexistence and automatic balance of exploration and exploitation, thus the population diversity
will be kept during the running. The algorithm model proposed here is between engineering technology and cognitive philosophy, it is not just a specific algorithm, but a kind of general methodology and/or a mode of thinking. The successful application of some realistic issues, like
distributed power generation optimization configuration, verified its applicability.