This paper starts with an updated review and analyzes recent developments in quantum-inspired algorithms for cybersecurity, with specific attention to possible perspectives of optimization. The enhancement of classical computing capabilities with qua...
This paper introduces a novel quantum-inspired algorithm for numerical multiobjective optimization, uniquely integrating the multilevel structure of qudits with principles of controlled thermonuclear fusion. Moving beyond conventional qubit-based app...
The automation of petroleum extraction columns requires robust and adaptive control due to the highly nonlinear nature of the heat and mass transfer processes involved. In this study, a hybrid control system integrating conventional fuzzy logic with...
Parameter calibration is critical for self-localization based on dead reckoning in the control of intelligent vehicles such as autonomous driving. Most traditional calibration methods for robotics control based on dead reckoning rely on data collecti...
This study proposes a new approach to identify structural parameters under seismic excitation using classical and quantum-inspired algorithms. Traditional methods often struggle with complex effects, noise, and computing limits. A five-story building...
We study the decomposition of large-scale Quadratic Unconstrained Binary Optimization Problems (QUBO) formulations for quantum and quantum-inspired annealers and propose two decomposition mechanisms. The first is one-way-one-hot (1W1H), which replace...
To address the challenge that unmanned surface vehicles (USVs) struggle to effectively balance tracking accuracy, control smoothness, and system energy efficiency under external disturbances, this paper proposes an anti-disturbance path tracking cont...
Non-convex stochastic optimization presents fundamental mathematical challenges across machine learning, wireless networks, data center resource allocation, and optical wireless communication systems, where complex loss landscapes with multiple local...
Alzheimer’s disease remains one of the hardest disorders to drug, and most computational pipelines still tackle one target at a time and ignore brain penetration until late. We bring all of that into a single optimization problem. Our framework...
Robust underwater target localization is essential for marine sensing and monitoring. However, RSS-based localization is affected by ray bending caused by sound-speed variations, anchor-node position perturbations, and non-line-of-sight (NLOS) propag...
The rapid growth of cloud and distributed computing systems has increased the complexity of real-time request distribution under dynamic and multi-factor conditions. In such environments, load-balancing decisions must simultaneously consider uncertai...
Recent developments in lithium-ion batteries have improved their capacity, which allows them to be used in more applications like power tools. However, they also carry higher risks, such as thermal runaway, which can happen if they are damaged. To ma...
This study introduces a novel Quantum-Inspired Hybrid Bald Eagle-Ukari Algorithm with Reinforcement Learning (QI-HBEUA-RL) for comprehensive optimization of conical solar distillers equipped with sand-filled copper conical fins. The proposed algorith...
The rapid growth of digital communication has intensified spam-related threats, including phishing and malware, which employ advanced evasion tactics. Traditional filtering methods struggle to keep pace, driving the need for sophisticated machine lea...
A key limitation of existing swarm intelligence (SI) algorithms for Node Coverage Optimization (NCO) is their inadequate solution accuracy. A novel chaotic quantum-inspired leader honey badger algorithm (CQLHBA) is proposed in this study. To enhance...
Objective solutions of multi-objective optimization problems (MOPs) are required to balance convergence and distribution to the Pareto front. This paper proposes a multi-objective quantum-inspired seagull optimization algorithm (MOQSOA) to optimize t...
Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (...
Accurate car-sharing demand prediction is a key factor in enhancing the operational efficiency of shared mobility systems. However, mobility data often exhibit temporal, spatial, and spatio-temporal interdependencies that pose significant challenges...
The quantum-inspired genetic algorithm (QGA), which combines quantum mechanics concepts and GA to enhance search capability, has been popular and provides an efficient search mechanism. This paper proposes a modified QGA, called dynamic QGA (DQGA). T...
Disaster logistics presents a highly complex decision-making challenge under conditions of uncertainty, where the timely and efficient allocation of scarce resources is essential to minimize human suffering. In this context, we propose a novel Quantu...
Wireless sensor networks (WSNs) play a major role in various applications, but the main challenge is to maintain security and balanced energy efficiency. Classical routing protocols struggle to achieve both energy efficiency and security because they...
In recent years, intelligent control of complex thermodynamic systems has gained increasing attention due to global demands for higher energy efficiency and reduced environmental impact in industrial settings. This study explores the integration of q...
This paper proposes a dynamic unmanned aerial vehicle (UAV) clustering model for multi-target localization in complex 3D environments, where mobility-aware cluster formation is integrated to enhance collaborative localization accuracy. We derive the...
The study introduces a novel standalone hybrid Energy Management System that combines solar PV, wind energy conversion systems, battery storage, and microturbines in order to provide reliable and efficient power under various operating conditions. Th...
Quantum computing has opened up various opportunities for the enhancement of computational power in the coming decades. We can design algorithms inspired by the principles of quantum computing, without implementing in quantum computing infrastructure...
The escalating sophistication of cyber threats underscores the critical need for intelligent and adaptive intrusion detection systems (IDSs) to identify known and novel attack vectors in real time. Feature selection is a key enabler of performance in...
Particle swarm optimization algorithm (PSO) is an effective metaheuristic that can determine Pareto-optimal solutions. We propose an extended PSO by introducing quantum gates in order to ensure the diversity of particle populations that are looking f...
The effective resolution of non-deterministic polynomial time hard (NP-hard) combinatorial optimization problems requires a delicate balance between global exploration and local exploitation. While Quantum-Inspired Algorithms (QIAs) leverage principl...
Electric scooter sharing mobility services have recently spread in major cities all around the world. However, the bad parking behavior of users has become a major source of issues, provoking accidents and compromising urban decorum of public areas....
The growing integration of renewable energy sources in grid-connected microgrids (MG) has made it increasingly challenging to attain the most cost-effective and emission-efficient power dispatch in the face of uncertainty. This study addresses the sc...
Establishing internet access for mobile ad hoc networks (MANET) is a job that is both vital and complex. MANET is used to build a broad range of applications, both commercial and non-commercial, with the majority of these apps obtaining access to int...
This study provides a comprehensive evaluation of bagging ensemble models for financial time series (FTS) classification and addresses a gap in the literature regarding how bootstrap methods, ensemble sizes, voting mechanisms, and loss functions join...
The high penetration of distributed renewable energy in rural microgrids imposes severe physical-layer fluctuations, weak information-layer communication, and limited computing-layer resources. These triple constraints create a fundamental tension: h...
This paper proposes a quantum-inspired evolutionary algorithm (QiEA) to solve an optimal service-matching task-assignment problem. Our proposed algorithm comes with the advantage of generating always feasible population individuals and, thus, elimina...
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, and local refinement...
In a chaotic system, deterministic, nonlinear, irregular, and initial-condition-sensitive features are desired. Due to its chaotic nature, it is difficult to quantify a chaotic system’s parameters. Parameter estimation is a major issue because...
A distributed database model can be effectively optimized through using query optimization. In such a model, the optimizer attempts to identify the most efficient join order, which minimizes the overall cost of the query plan. Successful query proces...
The knapsack problem is one of the most widely researched NP-complete combinatorial optimization problems and has numerous practical applications. This paper proposes a quantum-inspired differential evolution algorithm with grey wolf optimizer (QDGWO...
The rapid adoption of electric vehicles (EVs) and the increasing reliance on renewable energy sources necessitate innovative charging infrastructure solutions to address key challenges in energy efficiency, grid stability, and sustainable transportat...
IoT-Edge-Fog Computing presents a trio-logical model for decentralized computing in a time-sensitive manner. However, to address the rising need for real-time information processing and decision modeling, task allocation among dispersed Edge Computin...
This research explores the optimal operation of an offshore wave-powered hydrogen system, specifically designed to supply electricity and water to a bay in Humboldt, California, USA, and also sell it with hydrogen. The system incorporates a desalinat...
Optimization is a crucial challenge across various domains, including finance, resource allocation, and mobility. Quantum computing has the potential to redefine the way we handle complex problems by reducing computational complexity and enhancing so...
In many complex manufacturing environments, the running equipment must be monitored by Wireless Sensor Networks (WSNs), which not only requires WSNs to have long service lifetimes, but also to achieve rapid and high-quality transmission of equipment...
GVNS, which stands for General Variable Neighborhood Search, is an established and commonly used metaheuristic for the expeditious solution of optimization problems that belong to the NP-hard class. This paper introduces an expansion of the standard...
General variable neighborhood search (GVNS) is a well known and widely used metaheuristic for efficiently solving many NP-hard combinatorial optimization problems. We propose a novel extension of the conventional GVNS. Our approach incorporates ideas...
We analyze possible connections between quantum-inspired classifications and support vector machines. Quantum state discrimination and optimal quantum measurement are useful tools for classification problems. In order to use these tools, feature vect...
Image reconstruction and generation have become fundamental tasks in computer vision and quantum-inspired machine learning. Existing quantum-inspired variational autoencoders have demonstrated promising capabilities; however, they often suffer from l...
In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic clustering algorithms for this purpose has been contemplate...
The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environme...
Accurate and early detection of bone cancer is critical for improving patient outcomes, yet conventional radiographic interpretation remains limited by subjectivity and variability. Conventional AI models often struggle with complex multi-modal noise...
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