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Статті в журналах з теми "Classical optimization"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Bilimoria, K. D., E. M. Cliff, and H. J. Kelley. "Classical and neo-classical cruise-dash optimization." Journal of Aircraft 22, no. 7 (1985): 555–60. http://dx.doi.org/10.2514/3.45165.

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2

Moiseenko, V. V., and V. V. Yatskevich. "System optimization as a generalization of classical optimization." Cybernetics and Systems Analysis 33, no. 3 (1997): 416–19. http://dx.doi.org/10.1007/bf02733075.

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3

Erschen, Stefan, Fabian Duddeck, and Markus Zimmermann. "Robust Design using classical optimization." PAMM 15, no. 1 (2015): 565–66. http://dx.doi.org/10.1002/pamm.201510272.

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4

Barkoutsos, Panagiotis Kl, Giacomo Nannicini, Anton Robert, Ivano Tavernelli, and Stefan Woerner. "Improving Variational Quantum Optimization using CVaR." Quantum 4 (April 20, 2020): 256. http://dx.doi.org/10.22331/q-2020-04-20-256.

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Анотація:

Hybrid quantum/classical variational algorithms can be implemented on noisy intermediate-scale quantum computers and can be used to find solutions for combinatorial optimization problems. Approaches discussed in the literature minimize the expectation of the problem Hamiltonian for a parameterized trial quantum state. The expectation is estimated as the sample mean of a set of measurement outcomes, while the parameters of the trial state are optimized classically. This procedure is fully justified for quantum mechanical observables such as molecular energies. In the case of classical optimizat

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5

van Apeldoorn, Joran, András Gilyén, Sander Gribling, and Ronald de Wolf. "Convex optimization using quantum oracles." Quantum 4 (January 13, 2020): 220. http://dx.doi.org/10.22331/q-2020-01-13-220.

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We study to what extent quantum algorithms can speed up solving convex optimization problems. Following the classical literature we assume access to a convex set via various oracles, and we examine the efficiency of reductions between the different oracles. In particular, we show how a separation oracle can be implemented using O~(1) quantum queries to a membership oracle, which is an exponential quantum speed-up over the Ω(n) membership queries that are needed classically. We show that a quantum computer can very efficiently compute an approximate subgradient of a convex Lipschitz function. C

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6

Yu, G. "Min-Max Optimization of Several Classical Discrete Optimization Problems." Journal of Optimization Theory and Applications 98, no. 1 (1998): 221–42. http://dx.doi.org/10.1023/a:1022601301102.

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7

Amy, Matthew, and Joseph Lunderville. "Linear and Non-linear Relational Analyses for Quantum Program Optimization." Proceedings of the ACM on Programming Languages 9, POPL (2025): 1072–103. https://doi.org/10.1145/3704873.

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The phase folding optimization is a circuit optimization used in many quantum compilers as a fast and effective way of reducing the number of high-cost gates in a quantum circuit. However, existing formulations of the optimization rely on an exact, linear algebraic representation of the circuit, restricting the optimization to being performed on straightline quantum circuits or basic blocks in a larger quantum program. We show that the phase folding optimization can be re-cast as an affine relation analysis , which allows the direct application of classical techniques for affine relations to e

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8

Suh, M. W., J. M. Yu, and Je Hyeng Lee. "Crack Identification Using Classical Optimization Technique." Key Engineering Materials 183-187 (April 2000): 61–66. http://dx.doi.org/10.4028/www.scientific.net/kem.183-187.61.

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9

Ellerman, David P. "AN ARBITRAGE INTERPRETATION OF CLASSICAL OPTIMIZATION." Metroeconomica 41, no. 3 (1990): 259–76. http://dx.doi.org/10.1111/j.1467-999x.1990.tb00469.x.

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10

Boţ, Radu Ioan, Sorin-Mihai Grad, and Gert Wanka. "Classical linear vector optimization duality revisited." Optimization Letters 6, no. 1 (2010): 199–210. http://dx.doi.org/10.1007/s11590-010-0263-1.

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11

Ahmet, Demir, and Kose Utku. "BRAIN Journal - Solving Optimization Problems via Vortex Optimization Algorithm and Cognitive Development Optimization Algorithm." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 7, no. 4 (2016): 23–42. https://doi.org/10.5281/zenodo.1045013.

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Анотація:

ABSTRACT In the fields which require finding the most appropriate value, optimization became a vital approach to employ effective solutions. With the use of optimization techniques, many different fields in the modern life have found solutions to their real-world based problems. In this context, classical optimization techniques have had an important popularity. But after a while, more advanced optimization problems required the use of more effective techniques. At this point, Computer Science took an important role on providing software related techniques to improve the associated literature.

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12

Egger, Daniel J., Jakub Mareček, and Stefan Woerner. "Warm-starting quantum optimization." Quantum 5 (June 17, 2021): 479. http://dx.doi.org/10.22331/q-2021-06-17-479.

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Анотація:

There is an increasing interest in quantum algorithms for problems of integer programming and combinatorial optimization. Classical solvers for such problems employ relaxations, which replace binary variables with continuous ones, for instance in the form of higher-dimensional matrix-valued problems (semidefinite programming). Under the Unique Games Conjecture, these relaxations often provide the best performance ratios available classically in polynomial time. Here, we discuss how to warm-start quantum optimization with an initial state corresponding to the solution of a relaxation of a combi

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13

Luft, Daniel, and Volker Schulz. "Pre-shape calculus and its application to mesh quality optimization." Control and Cybernetics 50, no. 3 (2021): 263–301. http://dx.doi.org/10.2478/candc-2021-0019.

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Abstract Deformations of the computational mesh, arising from optimization routines, usually lead to decrease of mesh quality or even destruction of the mesh. We propose a theoretical framework using pre-shapes to generalize the classical shape optimization and calculus. We define pre-shape derivatives and derive corresponding structure and calculus theorems. In particular, tangential directions are featured in pre-shape derivatives, in contrast to classical shape derivatives, featuring only normal directions. Techniques from classical shape optimization and calculus are shown to carry over to

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14

Truger, Felix, Martin Beisel, Johanna Barzen, Frank Leymann, and Vladimir Yussupov. "Selection and Optimization of Hyperparameters in Warm-Started Quantum Optimization for the MaxCut Problem." Electronics 11, no. 7 (2022): 1033. http://dx.doi.org/10.3390/electronics11071033.

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Анотація:

Today’s quantum computers are limited in their capabilities, e.g., the size of executable quantum circuits. The Quantum Approximate Optimization Algorithm (QAOA) addresses these limitations and is, therefore, a promising candidate for achieving a near-term quantum advantage. Warm-starting can further improve QAOA by utilizing classically pre-computed approximations to achieve better solutions at a small circuit depth. However, warm-starting requirements often depend on the quantum algorithm and problem at hand. Warm-started QAOA (WS-QAOA) requires developers to understand how to select approac

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15

Lv, Xueying, Yitian Wang, Junyi Deng, Guanyu Zhang, and Liu Zhang. "Improved Particle Swarm Optimization Algorithm Based on Last-Eliminated Principle and Enhanced Information Sharing." Computational Intelligence and Neuroscience 2018 (December 5, 2018): 1–17. http://dx.doi.org/10.1155/2018/5025672.

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In this study, an improved eliminate particle swarm optimization (IEPSO) is proposed on the basis of the last-eliminated principle to solve optimization problems in engineering design. During optimization, the IEPSO enhances information communication among populations and maintains population diversity to overcome the limitations of classical optimization algorithms in solving multiparameter, strong coupling, and nonlinear engineering optimization problems. These limitations include advanced convergence and the tendency to easily fall into local optimization. The parameters involved in the imp

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16

Gabbassov, Einar. "Transit facility allocation: Hybrid quantum-classical optimization." PLOS ONE 17, no. 9 (2022): e0274632. http://dx.doi.org/10.1371/journal.pone.0274632.

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An essential consideration in urban transit facility planning is service efficiency and accessibility. Previous research has shown that reducing the number of facilities along a route may increase efficiency but decrease accessibility. Striking a balance between these two is a critical consideration in transit planning. Transit facility consolidation is a cost-effective way to improve the quality of service by strategically determining the desirable allocation of a limited number of facilities. This paper develops an optimization framework that integrates Geographical Information systems (GIS)

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17

Seipp, Jendrik, Thomas Keller, and Malte Helmert. "Saturated Post-hoc Optimization for Classical Planning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 11947–53. http://dx.doi.org/10.1609/aaai.v35i13.17419.

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Saturated cost partitioning and post-hoc optimization are two powerful cost partitioning algorithms for optimal classical planning. The main idea of saturated cost partitioning is to give each considered heuristic only the fraction of remaining operator costs that it needs to prove its estimates. We show how to apply this idea to post-hoc optimization and obtain a heuristic that dominates the original both in theory and on the IPC benchmarks.

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18

Hatomura, Takuya. "Iterative classical superadiabatic algorithm for combinatorial optimization." Journal of Physics A: Mathematical and Theoretical 53, no. 20 (2020): 205302. http://dx.doi.org/10.1088/1751-8121/ab83c7.

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19

Armstrong, Ronald D., Douglas H. Jones, and Zhaobo Wang. "Optimization of Classical Reliability in Test Construction." Journal of Educational and Behavioral Statistics 23, no. 1 (1998): 1–17. http://dx.doi.org/10.3102/10769986023001001.

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This article considers the problem of generating a test from an item bank using a criterion based on classical test theory parameters. A mathematical programming model is formulated that maximizes the reliability coefficient α, subject to logical constraints on the choice of items. The special structure of the problem is exploited with network theory and Lagrangian relaxation techniques. An empirical study shows that the method produces tests with high coefficient a subject to various practicable item constraints.

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20

Armstrong, Ronald D., Douglas H. Jones, and Zhaobo Wang. "Optimization of Classical Reliability in Test Construction." Journal of Educational and Behavioral Statistics 23, no. 1 (1998): 1. http://dx.doi.org/10.2307/1165345.

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21

ŠAMAJ, L., and J. K. PERCUS. "Gauge field optimization of classical fluid expansions." Molecular Physics 96, no. 3 (1999): 443–49. http://dx.doi.org/10.1080/00268979909482978.

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22

Rekha Gangula. "Hybrid Quantum-Classical Optimization: A Unified Framework for NISQ Applications." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 399–404. https://doi.org/10.52783/jisem.v10i43s.8387.

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Quantum machine learning (QML) holds transformative potential for solving classically intractable problems, yet its practical implementation on noisy intermediate-scale quantum (NISQ) devices remains hindered by two critical challenges: barren plateaus(exponentially vanishing gradients) and noise-induced gradient corruption. This paper introduces HyQ-OPT, a hybrid quantum-classical optimization framework that systematically addresses these limitations through three innovations: (1) quantum parameter-shift rules for unbiased gradient estimation, (2) noise-adaptive classical momentum to suppress

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23

Hasanagić, Redžo. "Optimization of thermal modification of wood by genetic algorithm and classical mathematical analysis." Journal of Forest Science 68, No. 2 (2022): 35–45. http://dx.doi.org/10.17221/95/2021-jfs.

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The use of wood in outdoor conditions is of great importance for the service life of wood, and the process of thermal modification (TM) directly affects the effective value of wood products. This paper presents theoretical and experimental studies of the parameters influencing TM of wood on the changes of its physical and mechanical properties. Experimental studies were performed on thermally modified wood samples for different values of the influential parameters of thermal modification: T (°C), t (h) and ρ (g·cm<sup>–3</sup>), while the tensile strength was obtained as the output

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24

Mataifa, H., S. Krishnamurthy, and C. Kriger. "Volt/VAR Optimization: A Survey of Classical and Heuristic Optimization Methods." IEEE Access 10 (2022): 13379–99. http://dx.doi.org/10.1109/access.2022.3146366.

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25

Pavolo, Domingo, and Delson Chikobvu. "Estimating Rubber Covered Conveyor Belting Cure Times Using Multiple Simultaneous Optimizations Ensemble." Operational Research in Engineering Sciences: Theory and Applications 5, no. 1 (2022): 90–106. http://dx.doi.org/10.31181/oresta180222016p.

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Multiple response surface methodology (MRSM) has been the favorite method for optimizing multiple response processes though it has two weaknesses which challenge the credibility of its solutions. The first weakness is the use of experimentally generated small sample size datasets, and the second is the selection, using classical model selection criteria, of single best models for each response for use in simultaneous optimization to obtain the optimum or desired solution. Classical model selection criteria do not always agree on the best model resulting in model uncertainty. The selection of s

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26

Augustino, Brandon, Giacomo Nannicini, Tamás Terlaky, and Luis F. Zuluaga. "Quantum Interior Point Methods for Semidefinite Optimization." Quantum 7 (September 11, 2023): 1110. http://dx.doi.org/10.22331/q-2023-09-11-1110.

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We present two quantum interior point methods for semidefinite optimization problems, building on recent advances in quantum linear system algorithms. The first scheme, more similar to a classical solution algorithm, computes an inexact search direction and is not guaranteed to explore only feasible points; the second scheme uses a nullspace representation of the Newton linear system to ensure feasibility even with inexact search directions. The second is a novel scheme that might seem impractical in the classical world, but it is well-suited for a hybrid quantum-classical setting. We show tha

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27

VERNIK, M. O. "COMPARISON OF CLASSICAL AND QUANTUM COMPUTING FOR PARTICLE SWARM OPTIMIZATION." Вісник Херсонського національного технічного університету, no. 2(89) (July 1, 2024): 134–38. http://dx.doi.org/10.35546/kntu2078-4481.2024.2.18.

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The article explored and delved into the advanced computational strategies of Particle Swarm Optimization (PSO) by contrasting classical and quantum computing paradigms. The advantages of quantum computing lie in its potential to solve computationally complex problems exponentially faster than classical computers. One of the advantages of Particle Swarm Optimization is its ability to find optimal solutions in complex search spaces. The research centers around the performance of PSO algorithms, as a part of the biological swarm optimization algorithms, when applied to a set of single-objective

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28

Laue, Sören, Mark Blacher, and Joachim Giesen. "Optimization for Classical Machine Learning Problems on the GPU." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7300–7308. http://dx.doi.org/10.1609/aaai.v36i7.20692.

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Анотація:

Constrained optimization problems arise frequently in classical machine learning. There exist frameworks addressing constrained optimization, for instance, CVXPY and GENO. However, in contrast to deep learning frameworks, GPU support is limited. Here, we extend the GENO framework to also solve constrained optimization problems on the GPU. The framework allows the user to specify constrained optimization problems in an easy-to-read modeling language. A solver is then automatically generated from this specification. When run on the GPU, the solver outperforms state-of-the-art approaches like CVX

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29

Mironenko, Vladimir, Elena Matsuro, and Catherine Ledovskikh. "Reverse engineering as a way to optimize and design parts produced by elastic-medium drawing." MATEC Web of Conferences 224 (2018): 01021. http://dx.doi.org/10.1051/matecconf/201822401021.

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On the example of an aeronautical part of the classical form for a box-type part, optimization of the cavity elastic forming process using a cover with guaranteed clearance is considered, as well as classical optimization of a part based on modeling and optimization based on the technology of “technological reconstruction”. Schemes of classical cavity elastic forming process and cavity elastic forming process with guaranteed clearance are presented. The main aspects of using NURBS technology for restoring the shape of a part from a finite element mesh are described. The problems arising in the

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30

Hastings, Matthew B. "Classical and quantum bounded depth approximation algorithms." quantum Information and Computation 19, no. 13&14 (2019): 1116–40. http://dx.doi.org/10.26421/qic19.13-14-3.

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We consider some classical and quantum approximate optimization algorithms with bounded depth. First, we define a class of "local" classical optimization algorithms and show that a single step version of these algorithms can achieve the same performance as the single step QAOA on MAX-3-LIN-2. Second, we show that this class of classical algorithms generalizes a class previously considered in the literature\cite{hirvonen2014large}, and also that a single step of the classical algorithm will outperform the single-step QAOA on all triangle-free MAX-CUT instances. In fact, for all but 4 choices of

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31

Sleesongsom, S., S. Yooyen, P. Prapamonthon, and S. Bureerat. "Reliability-based Design Optimization of Classical Wing Aeroelasticity." IOP Conference Series: Materials Science and Engineering 886 (July 28, 2020): 012015. http://dx.doi.org/10.1088/1757-899x/886/1/012015.

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32

Zheng, Dong-Qin, and Wei-Rong Zhong. "Efficiency optimization of the classical molecular heat pump." EPL (Europhysics Letters) 95, no. 2 (2011): 20005. http://dx.doi.org/10.1209/0295-5075/95/20005.

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33

Lebacque, Vassilissa, Vincent Jost, and Nadia Brauner. "Simultaneous optimization of classical objectives in JIT scheduling." European Journal of Operational Research 182, no. 1 (2007): 29–39. http://dx.doi.org/10.1016/j.ejor.2006.07.019.

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34

Gastaldo, Paolo, Sandro Ridella, and Rodolfo Zunino. "Prospects of quantum-classical optimization for digital design." Applied Mathematics and Computation 179, no. 2 (2006): 581–95. http://dx.doi.org/10.1016/j.amc.2005.11.129.

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35

Sweke, Ryan, Frederik Wilde, Johannes Jakob Meyer, et al. "Stochastic gradient descent for hybrid quantum-classical optimization." Quantum 4 (August 31, 2020): 314. http://dx.doi.org/10.22331/q-2020-08-31-314.

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Анотація:

Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of expectation values with respect to the outcome of parameterized quantum circuits. In this work, we explore the consequences of the prior observation that estimation of these quantities on quantum hardware results in a form of stochastic gradient descent optimization. We formalize this notion, which allows us to show that in many relevant cases, including VQE, QAOA and certain quantum classifiers, estimating expectation values with k measurement outcomes results in

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36

Fan, Lei, and Zhu Han. "Hybrid Quantum-Classical Computing for Future Network Optimization." IEEE Network 36, no. 5 (2022): 72–76. http://dx.doi.org/10.1109/mnet.001.2200150.

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37

Yukich, J. E. "Worst case asymptotics for some classical optimization problems." Combinatorica 16, no. 4 (1996): 575–86. http://dx.doi.org/10.1007/bf01271275.

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38

Sanaeva, Tatiana A., Anna A. Ilmushkina, and Osman M. Minaev. "HYBRID QUANTUM-CLASSICAL ALGORITHMS IN LOGISTICS OPTIMIZATION PROBLEMS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 3/13, no. 156 (2025): 240–45. https://doi.org/10.36871/ek.up.p.r.2025.03.13.027.

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Анотація:

The paper considers the application of hybrid quantum-classical algorithms (HQCA) to optimization problems in logistics, such as the vehicle routing problem (VRP), the traveling salesman problem (TSP), as well as their generalizations with time windows and fleet constraints. A multi-level architecture of hybrid computing is presented, including the stages of problem translation into QUBO form, implementation of variational quantum algorithms (QAOA), annealing on quantum systems (e.g., D-Wave Advantage2), as well as methods for error correction and post-processing of solutions. A review of dome

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39

B. Onuoha, Oluwaseun. "Integration of Modified Classical Conjugate Gradient Methods for Unconstrained Optimization." Arid-zone Journal of Basic and Applied Research 4, no. 1 (2024): 26–41. http://dx.doi.org/10.55639/607.474645.

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Анотація:

The integration of modified classical conjugate gradient methods (CGMs) for unconstrained optimization represents a crucial and evolving area of research within the field of optimization algorithms. Over time, numerous studies have put forth diverse modifications and novel approaches to enhance the effectiveness of classical CGMs. These modifications aim to address specific challenges and improve the overall performance of optimization algorithms in unconstrained scenarios. In order to tackle unconstrained optimization challenges and improve our understanding of their synergies, this ongoing s

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40

Rakesh Chowdary Ganta. "Quantum Computing: A Paradigm Shift in Distributed Systems Resource Optimization." Journal of Computer Science and Technology Studies 7, no. 4 (2025): 234–39. https://doi.org/10.32996/jcsts.2025.7.4.28.

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Анотація:

Quantum computing emerges as a revolutionary force in distributed systems optimization, fundamentally transforming resource allocation and system management paradigms. The integration of quantum algorithms with classical infrastructure introduces unprecedented capabilities in addressing complex optimization challenges in microservice architectures. Through quantum-enhanced protocols and hybrid quantum-classical systems, distributed computing achieves remarkable improvements in efficiency, scalability, and performance. The combination of Quantum Approximate Optimization Algorithm (QAOA) and Var

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41

Udrişte, Constantin, Mădălina Constantinescu, Ionel Ţevy, and Oltin Dogaru. "Dualities in Nonholonomic Optimization." Annals of West University of Timisoara - Mathematics and Computer Science 54, no. 2 (2016): 149–66. http://dx.doi.org/10.1515/awutm-2016-0020.

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Анотація:

Abstract This article deals with optimizing problems whose restrictions are nonholonomic. The central issue relates to dual nonholonomic programs (what they mean and how they are solved?) when the nonholonomic constraints are given by Pfaff equations. We emphasize that nonholonomic critical points are not the classical ones and that the nonholonomic Lagrange multipliers are not the classical (holonomic) Lagrange multipliers. Topological significance of Lagrange multipliers and dual function theory introduced by EDO and EDP are key results. Also new Riemannian geometries attached to a given non

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42

Sehrawat, Vikram. "Quantum Computing: Algorithms and Applications in Optimization Problems." Journal of Quantum Science and Technology 1, no. 2 (2024): 18–22. http://dx.doi.org/10.36676/jqst.v1.i2.11.

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Анотація:

Quantum computing has emerged as a transformative paradigm with the potential to revolutionize computational tasks previously deemed intractable for classical computers. This paper explores the application of quantum computing algorithms in solving optimization problems, a cornerstone of numerous fields including logistics, finance, machine learning, and operations research. Quantum computers harness quantum mechanical phenomena such as superposition and entanglement to perform computations in ways fundamentally different from classical computers. This enables quantum algorithms to explore vas

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43

Shaikh, Mohammad Shahnawaz, Prithviraj S. Chouhan, Imran Baig, and Syed Ibad Ali. "QUANTUM COMPUTING FOR OPTIMIZATION PROBLEMS: A REVIEW AND FUTURE DIRECTIONS." COMPUSOFT: An International Journal of Advanced Computer Technology 11 (June 22, 2022): 3995–97. https://doi.org/10.5281/zenodo.15087230.

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Quantum computing, leveraging principles of quantum mechanics, has shown potential in solving complex optimization problems more efficiently than classical approaches. This paper reviews recent advancements in quantum algorithms designed for optimization tasks and evaluates their performance against classical methods. We present a comprehensive analysis of quantum optimization algorithms such as Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing, discussing their applications, advantages, and limitations. Future directions are proposed, focusing on improving algorithm effi

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Laue, Sören, Matthias Mitterreiter, and Joachim Giesen. "GENO – Optimization for Classical Machine Learning Made Fast and Easy." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (2020): 13620–21. http://dx.doi.org/10.1609/aaai.v34i09.7097.

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Most problems from classical machine learning can be cast as an optimization problem. We introduce GENO (GENeric Optimization), a framework that lets the user specify a constrained or unconstrained optimization problem in an easy-to-read modeling language. GENO then generates a solver, i.e., Python code, that can solve this class of optimization problems. The generated solver is usually as fast as hand-written, problem-specific, and well-engineered solvers. Often the solvers generated by GENO are faster by a large margin compared to recently developed solvers that are tailored to a specific pr

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Ardelean, Sebastian Mihai, and Mihai Udrescu. "Hybrid quantum search with genetic algorithm optimization." PeerJ Computer Science 10 (August 5, 2024): e2210. http://dx.doi.org/10.7717/peerj-cs.2210.

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Quantum genetic algorithms (QGA) integrate genetic programming and quantum computing to address search and optimization problems. The standard strategy of the hybrid QGA approach is to add quantum resources to classical genetic algorithms (GA), thus improving their efficacy (i.e., quantum optimization of a classical algorithm). However, the extent of such improvements is still unclear. Conversely, Reduced Quantum Genetic Algorithm (RQGA) is a fully quantum algorithm that reduces the GA search for the best fitness in a population of potential solutions to running Grover’s algorithm. Unfortunate

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Bakó, Bence, Adam Glos, Özlem Salehi, and Zoltán Zimborás. "Prog-QAOA: Framework for resource-efficient quantum optimization through classical programs." Quantum 9 (March 20, 2025): 1663. https://doi.org/10.22331/q-2025-03-20-1663.

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Current state-of-the-art quantum optimization algorithms require representing the original problem as a binary optimization problem, which is then converted into an equivalent cost Hamiltonian suitable for the quantum device. Implementing each term of the cost Hamiltonian separately often results in high redundancy, significantly increasing the resources required. Instead, we propose to design classical programs for computing the objective function and certifying the constraints, and later compile them to quantum circuits, eliminating the reliance on the binary optimization problem representat

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Sabery, Ghulam Ali, Ghulam Hassan Danishyar, and Ghulam Sarwar Mubarez. "A Comparative Study of Metaheuristic Optimization Algorithms for Solving Engineering Design Problems." Journal of Mathematics and Statistics Studies 4, no. 4 (2023): 56–69. http://dx.doi.org/10.32996/jmss.2023.4.4.6.

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Metaheuristic optimization algorithms (Nature-Inspired Optimization Algorithms) are a class of algorithms that mimic the behavior of natural systems such as evolution process, swarm intelligence, human activity and physical phenomena to find the optimal solution. Since the introduction of meta-heuristic optimization algorithms, they have shown their profound impact in solving the high-scale and non-differentiable engineering problems. This paper presents a comparative study of the most widely used nature-inspired optimization algorithms for solving engineering classical design problems, which

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Hao, Shi Ming, and Li Zhi Cheng. "Improved Harmony Search Algorithm for Solving Optimization Problem with Mixed Discrete Variables." Applied Mechanics and Materials 325-326 (June 2013): 1485–88. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1485.

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The classical harmony search algorithm (HSA) can only be used to solve the unconstrained optimization problems with continuous decision variables. Therefore, the classical HSA is not suitable for solving an engineering optimization problem with mixed discrete variables. In order to improve the classical HSA, an engineering method for dealing with mixed discrete decision variables is introduced and an exact non-differentiable penalty function is used to transform the constrained optimization design model into an unconstrained mathematical model. Based on above improvements, a program of improve

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Muhamediyeva, Dilnoz, Nilufar Niyozmatova, Dilfuza Yusupova, and Boymirzo Samijonov. "Quantum optimization methods in water flow control." E3S Web of Conferences 590 (2024): 02003. http://dx.doi.org/10.1051/e3sconf/202459002003.

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This paper examines the problem of optimizing water flow control in order to minimize costs, represented as the square of the water flow. This takes into account restrictions on this flow, such as the maximum flow value. To solve this problem, two optimization methods are used: the classical optimization method Sequential Least SQuares Programming (SLSQP) and the quantum optimization method Variational Quantum Eigensolver (VQE). First, the classical SLSQP method finds the optimal control (water flow) according to the given cost function and constraints. Then the obtained result is refined usin

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Vishwakarma, Vinod Kumar. "Quantum Computing Algorithms for Nonlinear Optimization Problems." Communications on Applied Nonlinear Analysis 30, no. 4 (2023): 01–16. http://dx.doi.org/10.52783/cana.v30.279.

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The increasing complexity of real-world optimization problems highlights the importance of this research since classical algorithms are unable to provide efficient answers in these cases. Innovative methods for fast and scalable resolution of nonlinear optimization problems are required because these problems are prevalent in many fields. The potential for quantum computing to speed up optimization processes and overcome classical limitations is great, owing to its superposition principles and intrinsic parallelism. The integration of quantum algorithms (I-QA) into real-world applications, how

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