Qubit Adoption Method of a Quantum Computing-Based Metaheuristics Algorithm for Truss Structures Analysis DOI Creative Commons
Donwoo Lee, Seungjae Lee, Sudeok Shon

et al.

Biomimetics, Journal Year: 2023, Volume and Issue: 9(1), P. 11 - 11

Published: Dec. 27, 2023

Since the mention of Fourth Industrial Revolution in 2016, quantum computers and computing (QC) have emerged as key technologies. Many researchers are trying to realize computing. In particular, most development application metaheuristics algorithms using is focused on computer engineering fields. Cases which developed algorithm applied optimal design a building or results presented by expanding various directions very insufficient. Therefore, this paper, we proposed four methods adopting qubits perform pitch adjusting optimization process QbHS (quantum-based harmony search) it TTO (truss topology optimization) compare results. The same decreased number adopted iterations changes. As result applying methods, convergence performance differed depending adoption method, was superior conventional HS (harmony all methods. structural such QC expected contribute revitalization future technologies architectural field information systems.

Language: Английский

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations DOI Open Access
Mohammad H. Nadimi-Shahraki, Hoda Zamani, Zahra Asghari Varzaneh

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(7), P. 4113 - 4159

Published: May 27, 2023

Language: Английский

Citations

122
Quantum differential evolutionary algorithm with quantum-adaptive mutation strategy and population state evaluation framework for high-dimensional problems DOI
Wu Deng, Jiarui Wang,

Aibin Guo

et al.

Information Sciences, Journal Year: 2024, Volume and Issue: 676, P. 120787 - 120787

Published: May 31, 2024

Language: Английский

Citations

7
Scientometric analysis of quantum-inspired metaheuristic algorithms DOI Creative Commons

Pooja,

Sandeep K. Sood

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(2)

Published: Jan. 30, 2024

Abstract Quantum algorithms, based on the principles of quantum mechanics, offer significant parallel processing capabilities with a wide range applications. Nature-inspired stochastic optimization algorithms have long been research hotspot. The fusion mechanics methods can potentially address NP-hard problems more efficiently and exponentially faster. potential advantages provided by ground-breaking paradigm expedited scientific output quantum-inspired locale. Consequently, pertinent investigation is required to explain how advancements evolved. scientometric approach utilizes quantitative qualitative techniques analyze publications evaluate structure knowledge. Henceforth, current presents systematic analysis metaheuristic (QiMs) literature from Scopus database since its inception. implications article detailed exploration publication patterns, keyword co-occurrence network analysis, author co-citation country collaboration corresponding each opted category QiMs. reveals that QiMs solely account 26.66% share in computing experienced an impressive 42.59% growth rate past decade. Notably, power management, adiabatic computation, vehicle routing are prominent emerging application areas. An extensive identifies key insights gaps knowledge domain. Overall, findings provide cues researchers academic fraternity for identifying intellectual landscape latest trends QiMs, thereby fostering innovation informed decision-making.

Language: Английский

Citations

5
Quantum-Inspired Statistical Frameworks: Enhancing Traditional Methods with Quantum Principles DOI Creative Commons
Theodoros Kyriazos,

Mary Poga

Encyclopedia, Journal Year: 2025, Volume and Issue: 5(2), P. 48 - 48

Published: April 4, 2025

This manuscript introduces a comprehensive framework for augmenting classical statistical methodologies through the targeted integration of core quantum mechanical principles—specifically superposition, entanglement, measurement, wavefunctions, and density matrices. By concentrating on these foundational concepts instead whole expanse theory, we propose “quantum-inspired” models that address persistent shortcomings in conventional approaches. In particular, five pivotal distributions (normal, binomial, Poisson, Student’s t, chi-square) are reformulated to incorporate interference terms, phase factors, operator-based transformations, thereby facilitating representation multimodal data, phase-sensitive dependencies, correlated event patterns—characteristics frequently underrepresented purely real-valued, frameworks. Furthermore, ten quantum-inspired principles delineated guide practitioners systematically adapting mechanics traditional inferential tasks. These illustrated domain-specific applications finance, cryptography (distinct from direct applications), healthcare, climate modeling, demonstrating how amplitude-based confidence measures, matrices, measurement analogies can enrich standard by capturing more nuanced correlation structures enhancing predictive performance. unifying constructs with established this work underscores potential interdisciplinary collaboration paves way advanced data analysis tools capable addressing high-dimensional, complex, dynamically evolving datasets. Complete R code ensures reproducibility further exploration.

Language: Английский

Citations

0
Quantum-Inspired Coalition Formation Techniques in Multi-agent Systems for Human Centric Applications—A Review DOI
Rupali Mitra, Romit S. Beed, Tamal Chakraborty

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Quantum Snowflake Algorithm (QSA): A Snowflake-Inspired, Quantum-Driven Metaheuristic for Large-Scale Continuous and Discrete Optimization with Application to the Traveling Salesman Problem DOI Creative Commons
Zeki Oralhan, Burcu Oralhan

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(9), P. 5117 - 5117

Published: May 4, 2025

The Quantum Snowflake Algorithm (QSA) is a novel metaheuristic for both continuous and discrete optimization problems, combining collision-based diversity, quantum-inspired tunneling, superposition-based partial solution sharing, local refinement steps. QSA embeds candidate solutions in auxiliary space, where collision operators ensure that agents—snowflakes—reject each other remain diverse. This approach inspired by snowflakes which prevent collisions while retaining unique crystalline patterns. Large leaps to escape deep minima are simultaneously provided quantum particularly useful highly multimodal environments. Tests on challenging functions like Lévy HyperSphere showed the can more reliably obtain very low objective values domains than conventional swarm or evolutionary approaches. A 200-city Traveling Salesman Problem (TSP) confirmed excellent tour quality of optimization. It drastically reduces route length compared Artificial Bee Colony (ABC), Genetic (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), (QPSO), Cuckoo Search (CS). These results show tunneling accelerates from traps, superposition search increase exploitation, repulsion maintains population diversity. Together, these elements provide well-rounded method easy adapt different problem areas. In order establish as versatile framework range large-scale challenges, future research could investigate multi-objective extensions, adaptive parameter control, domain-specific hybridisations.

Language: Английский

Citations

0
A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer’s disease detection DOI Creative Commons

Ilknur Sercek,

Niranjana Sampathila, İrem Taşçı

et al.

Cognitive Neurodynamics, Journal Year: 2025, Volume and Issue: 19(1)

Published: May 10, 2025

Abstract Alzheimer's disease (AD) is a common cause of dementia. We aimed to develop computationally efficient yet accurate feature engineering model for AD detection based on electroencephalography (EEG) signal inputs. New method: retrospectively analyzed the EEG records 134 and 113 non-AD patients. To generate multilevel features, discrete wavelet transform was used decompose input EEG-signals. devised novel quantum-inspired EEG-signal extraction function 7-distinct different subgraphs Goldner-Harary pattern (GHPat), selectively assigned specific subgraph, using forward-forward distance-based fitness function, each block textural extraction. extracted statistical features standard moments, which we then merged with features. Other components were iterative neighborhood component analysis selection, shallow k-nearest neighbors, as well majority voting greedy algorithm additional voted prediction vectors select best overall results. With leave-one-subject-out cross-validation (LOSO CV), our attained 88.17% accuracy. Accuracy results stratified by channel lead placement brain regions suggested P4 parietal region be most impactful. Comparison existing methods: The proposed outperforms methods achieving higher accuracy approach, ensuring robustness generalizability. Cortex maps generated that allowed visual correlation channel-wise various regions, enhancing explainability.

Language: Английский

Citations

0
Quantum-inspired meta-heuristic approaches for a constrained portfolio optimization problem DOI
Abhishek Gunjan, Siddhartha Bhattacharyya

Evolutionary Intelligence, Journal Year: 2024, Volume and Issue: 17(4), P. 3061 - 3100

Published: March 25, 2024

Language: Английский

Citations

2
Quantum computing and quantum-inspired techniques for feature subset selection: a review DOI
Ashis Mandal, Basabi Chakraborty

Knowledge and Information Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 17, 2024

Language: Английский

Citations

2
A Modified Quantum-Inspired Genetic Algorithm Using Lengthening Chromosome Size and an Adaptive Look-Up Table to Avoid Local Optima DOI Creative Commons

Shahin Hakemi,

Mahboobeh Houshmand, Seyyed Abed Hosseini

et al.

Axioms, Journal Year: 2023, Volume and Issue: 12(10), P. 978 - 978

Published: Oct. 17, 2023

The quantum-inspired genetic algorithm (QGA), which combines quantum mechanics concepts and GA to enhance search capability, has been popular provides an efficient mechanism. This paper proposes a modified QGA, called dynamic QGA (DQGA). proposed utilizes lengthening chromosome strategy for balanced smooth transition between exploration exploitation phases avoid local optima premature convergence. Apart from that, novel adaptive look-up table rotation gates is presented boost the algorithm’s optimization abilities. To evaluate effectiveness of these ideas, DQGA tested by various mathematical benchmark functions as well real-world constrained engineering problems against several well-known state-of-the-art algorithms. obtained results indicate merits its superiority solving multimodal problems.

Language: Английский

Citations

5