Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Yazdani, Danial"'
Autor:
Yazdani, Danial, Branke, Juergen, Khorshidi, Mohammad Sadegh, Omidvar, Mohammad Nabi, Li, Xiaodong, Gandomi, Amir H., Yao, Xin
Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems. While meta-heuristics have shown promising effectiven
Externí odkaz:
http://arxiv.org/abs/2402.15731
Autor:
Khorshidi, Mohammad Sadegh, Yazdanjue, Navid, Gharoun, Hassan, Yazdani, Danial, Nikoo, Mohammad Reza, Chen, Fang, Gandomi, Amir H.
In machine learning, the exponential growth of data and the associated ``curse of dimensionality'' pose significant challenges, particularly with expansive yet sparse datasets. Addressing these challenges, multi-view ensemble learning (MEL) has emerg
Externí odkaz:
http://arxiv.org/abs/2401.06251
As optimization challenges continue to evolve, so too must our tools and understanding. To effectively assess, validate, and compare optimization algorithms, it is crucial to use a benchmark test suite that encompasses a diverse range of problem inst
Externí odkaz:
http://arxiv.org/abs/2312.07083
This document introduces a set of 24 box-constrained numerical global optimization problem instances, systematically constructed using the Generalized Numerical Benchmark Generator (GNBG). These instances cover a broad spectrum of problem features, i
Externí odkaz:
http://arxiv.org/abs/2312.07034
Autor:
Peng, Mai, She, Zeneng, Yazdani, Delaram, Yazdani, Danial, Luo, Wenjian, Li, Changhe, Branke, Juergen, Nguyen, Trung Thanh, Gandomi, Amir H., Jin, Yaochu, Yao, Xin
Many real-world optimization problems possess dynamic characteristics. Evolutionary dynamic optimization algorithms (EDOAs) aim to tackle the challenges associated with dynamic optimization problems. Looking at the existing works, the results reporte
Externí odkaz:
http://arxiv.org/abs/2308.12644
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, Luo, Wenjian, Omidvar, Mohammad Nabi, Gandomi, Amir H., Nguyen, Trung Thanh, Branke, Juergen, Li, Xiaodong, Yang, Shengxiang, Yao, Xin
This document introduces the Generalized Moving Peaks Benchmark (GMPB), a tool for generating continuous dynamic optimization problem instances that is used for the CEC 2024 Competition on Dynamic Optimization. GMPB is adept at generating landscapes
Externí odkaz:
http://arxiv.org/abs/2106.06174
The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence,
Externí odkaz:
http://arxiv.org/abs/2011.05700
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Yazdani, Danial, Omidvar, Mohammad Nabi, Deplano, Igor, Lersteau, Charly, Makki, Ahmed, Wang, Jin, Nguyen, Trung Thanh
Publikováno v:
In Transportation Research Part C June 2019 103:158-173
