Results 61 to 70 of about 5,749 (206)

A Review of Benchmark and Test Functions for Global Optimization Algorithms and Metaheuristics

WIREs Computational Statistics, Volume 17, Issue 2, June 2025.
ABSTRACT Benchmarking in optimization is a critical step in evaluating the performance, robustness, and scalability of machine learning algorithms and metaheuristics. While trends in benchmark design continue to evolve, synthetic functions remain vital for fundamental stress tests and theoretical evaluations.
M. Z. Naser, Mohammad Khaled Al‐Bashiti, Arash Teymori Gharah Tapeh, Ahmad Naser, Venkatesh Kodur, Rami Hawileh, Jamal Abdalla, Nima Khodadadi, Amir H. Gandomi, Armin Dadras Eslamlou   +9 more
wiley   +1 more source

Topological search and gradient descent boosted Runge–Kutta optimiser with application to engineering design and feature selection

CAAI Transactions on Intelligence Technology, Volume 10, Issue 2, Page 557-614, April 2025.
Abstract The Runge–Kutta optimiser (RUN) algorithm, renowned for its powerful optimisation capabilities, faces challenges in dealing with increasing complexity in real‐world problems. Specifically, it shows deficiencies in terms of limited local exploration capabilities and less precise solutions.
Jinge Shi, Yi Chen, Ali Asghar Heidari, Zhennao Cai, Huiling Chen, Guoxi Liang   +5 more
wiley   +1 more source

Cryptanalysis on Two Kinds of Number Theoretic Pseudo‐Random Generators Using Coppersmith Method

IET Information Security, Volume 2025, Issue 1, 2025.
Pseudo‐random number generator (PRNG) is a type of algorithm that generates a sequence of random numbers using a mathematical formula, which is widely used in computer science, such as simulation, modeling applications, data encryption, et cetera. The efficiency and security of PRNG are closely related to its output bits at each iteration.
Ran Zhang, Jingguo Bi, Lixiang Li, Haipeng Peng, Qichun Wang   +4 more
wiley   +1 more source

An Accelerated Fixed-Point Algorithm Applied to Quadratic Convex Separable Knapsack Problems

Journal of Control Science and Engineering
In this article, we propose a root-finding algorithm for solving a quadratic convex separable knapsack problem, which is more straightforward than existing methods and competitive in practice.
Atécio Alves, Jônatas O. L. Silva, Luiz C. Matioli, Paulo S. M. Santos, Sissy S. Souza   +4 more
doaj   +1 more source

Postprocessing Variationally Scheduled Quantum Algorithm for Constrained Combinatorial Optimization Problems

IEEE Transactions on Quantum Engineering
In this article, we propose a postprocessing variationally scheduled quantum algorithm (pVSQA) for solving constrained combinatorial optimization problems (COPs).
Tatsuhiko Shirai, Nozomu Togawa
doaj   +1 more source

Algorithm Engineering in Robust Optimization

, 2015
Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust ...
A Agra, A Atamtürk, A Beck, A Ben-Tal, A Ben-Tal, A Ben-Tal, A Ben-Tal, A Ben-Tal, A Ben-Tal, A Ben-Tal, A Ben-Tal, A Ben-Tal, A Ben-Tal, A Erera, A Kasperski, A Kasperski, A Kasperski, A Kasperski, A Mutapcic, A Ouorou, A Sbihi, A Schöbel, A Soyster, A Takeda, AB Chassein, B Roy, C Bohle, C Büsing, C Büsing, C Liebchen, D Bertsimas, D Bertsimas, D Bertsimas, D Bertsimas, D Bertsimas, D Bertsimas, D Bertsimas, D Bertsimas, D Catanzaro, D Delling, D Golovin, DJ Alem, DJ Thuente, E Conde, E Conde, E Conde, E Conde, F Bruns, F Pérez-Galarce, F Taniguchi, G Calafiore, G Calafiore, GC Calafiore, H Aissi, H Aissi, H Iida, H Mausser, H Yaman, HE Mausser, I Averbakh, I Averbakh, J Goh, J Lin, J Löfberg, J Pereira, J Xu, JE Falk, JLG Velarde, JM Mulvey, JR Farias de, KS Goetzmann, L Jenkins, LE Ghaoui, M Bürger, M Fischetti, M Fischetti, M Fischetti, M Goerigk, M Goerigk, M Goerigk, M Goerigk, M Goerigk, M Goerigk, M Goerigk, M Goerigk, M Goerigk, M Inuiguchi, M López, M Monaci, M Monaci, MC Campi, MK Zanjani, N Eggenberg, O Berman, P Adasme, P Kouvelis, P Sanders, P Zieliński, PC Bouman, R Kalai, R Khandekar, R Montemanni, R Montemanni, R Montemanni, R Reemtsen, S Cicerone, S Siddiqui, S Suzuki, TS Ng, U Feige, V Cacchiani, V Jeyakumar, X Song, Y Nikulin, Y Yin   +114 more
core   +1 more source

Fast algorithm for quadratic knapsack problem

Vestnik of Saint Petersburg University. Mathematics. Mechanics. Astronomy, 2022
The paper considers a quadratic programming problem with a strictly convex separable objective function, a single linear constraint, and two-sided constraints on variables. This problem is commonly called the Convex Knapsack Separable Quadratic Problem, or CKSQP for short.
openaire   +1 more source

Optimization of micro grid with distributed energy resources using physics based meta heuristic techniques

IET Renewable Power Generation, Volume 19, Issue 1, January/December 2025.
Optimization of proposed micro grid system is done using physics based meta heuristic methods. A grid connected micro grid consist of solar PV system, wind turbine (WT), micro gas turbine (MT), fuel cell (FC) and battery energy storage system (BESS).
Jayati Vaish, Anil Kumar Tiwari, Khadim Moin Siddiqui   +2 more
wiley   +1 more source

Lagrangian duality in quantum optimization: Overcoming QUBO limitations for constrained problems

Physical Review Research
We propose an approach to solving constrained combinatorial optimization problems based on embedding the concept of Lagrangian duality into the framework of adiabatic quantum computation.
Einar Gabbassov, Gili Rosenberg, Artur Scherer   +2 more
doaj   +1 more source

Cons-training tensor networks: Embedding and optimization over discrete linear constraints

SciPost Physics
In this study, we introduce a novel family of tensor networks, termed constrained matrix product states (MPS), designed to incorporate exactly arbitrary discrete linear constraints, including inequalities, into sparse block structures.
Javier Lopez-Piqueres, Jing Chen
doaj   +1 more source