Zobrazeno 1 - 10
of 559
pro vyhledávání: '"moving peaks benchmark"'
Autor:
Omidvar, Mohammad Nabi, Yazdani, Danial, Branke, Juergen, Li, Xiaodong, Yang, Shengxiang, Yao, Xin
This document describes the generalized moving peaks benchmark (GMPB) and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems. It presents a set of 15 benchmark problems, the relevant source code,
Externí odkaz:
http://arxiv.org/abs/2107.11019
Autor:
Yazdani, Danial, Mavrovouniotis, Michalis, Li, Changhe, Chen, Guoyu, Luo, Wenjian, Omidvar, Mohammad Nabi, Branke, Juergen, Yang, Shengxiang, Yao, Xin
The Generalized Moving Peaks Benchmark (GMPB) is a tool for generating continuous dynamic optimization problem instances with controllable dynamic and morphological characteristics. GMPB has been used in recent Competitions on Dynamic Optimization at
Externí odkaz:
http://arxiv.org/abs/2106.06174
Publikováno v:
In Swarm and Evolutionary Computation October 2022 74
open access article Prediction in evolutionary dynamic optimization (EDO), such as predicting the movement of optima, or when and how an environment will change, is a topic that is still under investigation and presents unsolved challenges. A few stu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56e9b9b989a9559dac1829a78adf2079
https://cronfa.swan.ac.uk/Record/cronfa60896/Download/60896__25202__f898f6447c944691abfbbf7c6a0fc51e.pdf
https://cronfa.swan.ac.uk/Record/cronfa60896/Download/60896__25202__f898f6447c944691abfbbf7c6a0fc51e.pdf
Autor:
Yazdani, Danial, Branke, Juergen, Omidvar, Mohammad Nabi, Li, Xiaodong, Li, Changhe, Mavrovouniotis, Michalis, Nguyen, Trung Thanh, Yang, Shengxiang, Yao, Xin
This document describes the Generalized Moving Peaks Benchmark (GMPB) that generates continuous dynamic optimization problem instances. The landscapes generated by GMPB are constructed by assembling several components with a variety of controllable c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b065f34e3ef23e44dcc574657644e92
http://arxiv.org/abs/2106.06174
http://arxiv.org/abs/2106.06174
Kniha
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Raymond Chiong, Irene Moser
Publikováno v:
Metaheuristics for Dynamic Optimization ISBN: 9783642306648
Metaheuristics for Dynamic Optimization
Metaheuristics for Dynamic Optimization
Many practical, real-world applications have dynamic features. If the changes in the fitness function of an optimization problem are moderate, a complete restart of the optimization algorithm may not be warranted. In those cases, it is meaningful to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82fa88d87c720221175c1cd0f0837810
https://doi.org/10.1007/978-3-642-30665-5_3
https://doi.org/10.1007/978-3-642-30665-5_3
Autor:
majid mohammadpour, hamid parvin
Publikováno v:
مجله مدل سازی در مهندسی, Vol 15, Iss 51, Pp 113-132 (2017)
Artificial Bee Colony Algorithm(ABC) is one of the swarm intelligence optimization algorithms that is extensively used for the goals and applications static. Many practical, real-world applications, nevertheless, are dynamic. Thus we need to get used
Externí odkaz:
https://doaj.org/article/f59609c017224b8a9fb735cf8929cad9
Publikováno v:
Journal of Artificial Intelligence and Data Mining, Vol 6, Iss 1, Pp 191-205 (2018)
Many of the problems considered in optimization and learning assume that solutions exist in a dynamic. Hence, algorithms are required that dynamically adapt with the problem’s conditions and search new conditions. Mostly, utilization of information
Externí odkaz:
https://doaj.org/article/bdfd3ffd26494e6dac4a4e6f0a47bc0b