Continuous optimization models / by 242970 Eiselt, Horst A., Pederzoli, G., Sandblom, C. L.

The Complexity of Continuous Optimization by Rogaway, Phillip

“…This represents an unusual interplay of discrete and continuous mathematics: using a combinatorial argument to get a hardness result for a continuous optimization problem.…”
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Algorithms for continuous optimization : the state of the art / by Spedicato, Emilio

Continuous optimization : current trends and modern applications / by Rubinov, Aleksandr Moiseevich, Jeyakumar, Vaithilingam

Hybrid harmony search algorithm for continuous optimization problems by Ala’a Atallah, Hamad Alomoush

“…Taken together, the proposed hybrid algorithm, called IHS-GWO, aims to address continuous optimization problems. The IHS-GWO is evaluated using two standard benchmarking sets and two real-world optimization problems. …”
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The Algorithm of Continuous Optimization Based on the Modified Cellular Automaton by Oleg Evsutin, Alexander Shelupanov, Roman Meshcheryakov, Dmitry Bondarenko, Angelika Rashchupkina

Subjects: “…continuous optimization…”
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Parameterization Techniques for Quadratic Continuous Optimal Control Problems by Saba S. Hasan, Falah A. Khalaf, Jabbar A. Eleiwy

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Global Continuous Optimization with Error Bound and Fast Convergence by Maruyama, Yu, Zheng, Xiaoyu, Kawaguchi, Kenji

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Visible imaging system optical design by continuous optimization of glasses by Héron Sébastien, Semet Yann

“…This paper proposes an alternative method to avoid the complicated discrete variables optimization process thanks to a two-steps continuous optimization methodology starting with fictitious glasses models before jumping to the real glasses optimization design. …”
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Twenty years of EUROPT, the EURO working group on Continuous Optimization by Sonia Cafieri, Tatiana Tchemisova, Gerhard-Wilhelm Weber

Subjects: “…Continuous Optimization…”
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Discrete-continuous optimization for robot perception via semidefinite relaxation by Hu, Siyi,S.M.Massachusetts Institute of Technology.

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Nested Markov chain hyper-heuristic (NMHH): a hybrid hyper-heuristic framework for single-objective continuous problems by Nándor Bándi, Noémi Gaskó

Subjects: “…Continuous optimization…”
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Enhancing three variants of harmony search algorithm for continuous optimization problems by Alomoush, Alaa A., Alsewari, Abdulrahman A., Kamal Z., Zamli, Alrosan, Ayat, Alomoush, Waleed, Alissa, Khalid

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Enhancing Differential Evolution on Continuous Optimization Problems by Detecting Promising Leaders by Yiqiao Cai, Duanwei Wu, Shaopeng Liu, Shunkai Fu, Peizhong Liu

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An Adaptive Dimension Weighting Spherical Evolution to Solve Continuous Optimization Problems by Yifei Yang, Sichen Tao, Shibo Dong, Masahiro Nomura, Zheng Tang

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EUROpt, the Continuous Optimization Working Group of EURO: From idea to maturity by Tibor Illés, Tamás Terlaky

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Flows, Submodularity, Sparsity, and Beyond: Continuous Optimization Insights for Discrete Problems by Axiotis, Kyriakos

“…In this thesis we build on connections between discrete and continuous optimization. In the first part of the thesis we propose faster second-order convex optimization algorithms for classical graph algorithmic problems. …”
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A novel hybrid bat algorithm for solving continuous optimization problems by Liu, Qi, Wu, Lei, Xiao, Wensheng, Wang, Fengde, Zhang, Linchuan

“…The Bat Algorithm (BA), which is a global optimization method, performs poorly on complex continuous optimization problems due to BA’s disadvantages such as the premature convergence problem. …”
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Sampling-based adaptive bounding evolutionary algorithm for continuous optimization problems by Luo, Linbo, Hou, Xiangting, Zhong, Jinghui, Cai, Wentong, Ma, Jianfeng

“…This paper proposes a novel sampling-based adaptive bounding evolutionary algorithm termed SABEA that is capable of dynamically updating the search space during the evolution process for continuous optimization problems. The proposed SABEA adopts two bounding strategies, namely fitness-based bounding and probabilistic sampling-based bounding, to select a set of individuals over multiple generations and leverage the value information from these individuals to update the search space of a given problem for improving the solution accuracy and search efficiency. …”
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Keenness for characterizing continuous optimization problems and predicting differential evolution algorithm performance by Yaxin Li, Jing Liang, Kunjie Yu, Caitong Yue, Yingjie Zhang

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