Quantum neural networks (QNNs) represent an emerging technology that uses a quantum computer for neural network computations. The QNNs have demonstrated potential advantages over classical neural networks in certain tasks. As a core component of a QN...
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...
An automated design approach using an evolutionary algorithm for the development of quantum cascade lasers (QCLs) is presented. Our algorithmic approach merges computational intelligence techniques with the physics of device structures, representing...
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...
Integrated energy systems have become an important research topic in the pursuit of sustainable energy development. This paper examines regional integrated energy systems, presents the typical architecture of regional integrated energy systems, and b...
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...
Genetic algorithms (GAs) are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical p...
Quantum neural architecture search (QNAS) has emerged as a promising approach for automatically designing parameterized quantum circuits (PQCs) for near-term quantum machine learning tasks. However, quantum evolutionary algorithm (QEA)-based QNAS met...
Optimization has always been viewed as a central component of many electrical engineering techniques, where it involves designing a complex system with various constraints and competing objectives. The method described in this work proposes a hybrid...
The selection of the right topology (ansatz) for a Variational Quantum Algorithm (VQA) is a complex task that usually involves deep knowledge of a particular problem. The importance of the selection is greater when we consider the current state of qu...
Recent work on hybrid quantum-classical machine learning systems has demonstrated success in utilizing parameterized quantum circuits (PQCs) to solve the challenging reinforcement learning (RL) tasks, with provable learning advantages over classical...
Leukocytes, also referred to as white blood cells (WBCs), are a crucial component of the human immune system. Abnormal proliferation of leukocytes in the bone marrow leads to leukemia, a fatal blood cancer. Classification of various subtypes of WBCs...
The combination and convergence of energy-intensive industries developed by ecological factors based on energy clusters is discussed in this paper. Here, a few models for the prediction of greenhouse effects are used as a single type of modeling. In...
The main objective of short-term hydrothermal scheduling is the optimal allocation of the hydro and thermal generating units, so that the total cost of thermal plants can be minimized. The time of operation of the functioning units are considered to...
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...
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 majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism...
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...
Medical optical imaging, with the aid of the “terahertz tomography”, is a novel medical imaging technique based on the electromagnetic waves. Such advanced imaging techniques strive for the detailed theoretical and computational analysis...
Recently, a new field that combines metaheuristic algorithms and quantum computing has been created and is being applied to optimization problems in various fields. However, the application of quantum computing-based metaheuristic algorithms to the o...
The paper focuses on the opportunity of the application of the quantum-inspired evolutionary algorithm for determining minimal costs of the assignment in the quadratic assignment problem. The idea behind the paper is to present how the algorithm has...
Evolutionary systems (ES) include software applications that solve problems using heuristic methods instead of the deterministic ones. The classical computing used for ES development involves random methods to improve different kinds of genomes. The...
With the development of various metaheuristic algorithms, research cases that perform weight optimization of truss structures are steadily progressing. In particular, due to the possibility of developing quantum computers, metaheuristic algorithms co...
The particle swarm optimization algorithm (PSO) is not good at dealing with discrete optimization problems, and for the krill herd algorithm (KH), the ability of local search is relatively poor. In this paper, we optimized PSO by quantum behavior and...
Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu...
In existing forecasting research papers support vector regression with chaotic mapping function and evolutionary algorithms have shown their advantages in terms of forecasting accuracy improvement. However, for classical particle swarm optimization (...
Many applications of quantum computers require the classical and quantum implementation of dynamic systems (DSs). These applications comprise interacting quantum and classical tasks. While quantum tasks evolve in the quantum domain, classical tasks b...
The symbiotic organism search (SOS) algorithm is a promising meta-heuristic evolutionary algorithm. Its excellent quality of global optimization solution has aroused the interest of many researchers. In this work, we not only applied the strategy of...
The rapid advancement of quantum computing poses a severe threat to traditional public key cryptosystems. Lattice-based cryptography has emerged as a core candidate for post-quantum cryptography due to its presumed quantum resistance, robust security...
Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with...
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...
The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has shown remarkable effectiveness in solving multi-objective problems (MOPs). In this paper, we integrate the quantum-behaved particle swarm optimization (QPSO) algorithm with t...
Surface roughness is a significant factor in determining the product quality and highly impacts the production price. The ability to predict the surface roughness before production would save the time and resources of the process. This research inves...
Quantum convolutional neural networks (QCNNs) are emerging as promising models in quantum machine learning, particularly for image classification and computer vision tasks. Recent developments include hybrid classical–quantum architectures, adv...
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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...
Quantum computer deployment in space remains constrained by demanding hardware, environmental, and communications requirements. This paper reviews the opportunities and challenges of using quantum computing in space systems. It focuses on payload int...
Differential cryptanalysis is a fundamental technique in symmetric-key cryptanalysis. While the existing literature and several surveys have separately addressed classical differential attacks, deep learning-assisted cryptanalysis, and quantum-relate...
We present an evolutionary optimization framework for designing neural architectures for holographic phase prediction in optical tweezer arrays. The framework combines Fourier-transform-based network structures with genetic optimization of layer comp...
As the fields of engineering, energy, and geology become increasingly complex, decision makers face escalating challenges that require skilled solutions to meet practical production needs. Evolutionary algorithms, inspired by biological evolution, ha...
An extended membrane system using a tissue-like P system with evolutional symport/antiport rules and a promoter/inhibitor, which is based on the evolutionary mechanism of quantum-behaved particle swarm optimization (QPSO) and improved QPSO, named CQP...
In the era of big data, the size and complexity of the data are increasing especially for those stored in remote locations, and whose difficulty is further increased by the ongoing rapid accumulation of data scale. Real-world optimization problems pr...
Using the bilevel optimization (BIO) scheme, this paper presents a time-optimal path planner for autonomous underwater vehicles (AUVs) operating in grid-based environments with ocean currents. In this scheme, the upper optimization problem is defined...
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...
Background: Gastrointestinal (GI) cancers remain a major global health concern, where timely and accurate interpretation of endoscopic findings plays a decisive role in patient outcomes. In recent years, deep learning–based decision support sys...
As humanity struggles to find a path to resilience amidst global change vagaries, understanding organizing principles of living systems as the pillar for human existence is rapidly growing in importance. However, finding quantitative definitions for...
Identifying the most reactive conformation of a molecule is a central challenge in computational chemistry, particularly when reactivity depends on subtle conformational effects. While most conformation search tools aim to find the lowest-energy stru...
Cyber defense has evolved into an algorithmically intensive discipline where mathematical rigor and adaptive computation underpin the robustness and continuity of digital infrastructures. This review consolidates the algorithmic spectrum that support...
Research on fault identification for wind turbines (WTs) is a widespread concern. However, the identification accuracy in existing research is vulnerable to uncertainty in the operation data, and the identification results lack interpretability. In t...
Modern developments in data analysis techniques and evolutionary optimization algorithms have made it possible to analyze large amounts of unstructured digital data sets. Based on the differential evolution algorithm and semiclassical quantum simulat...
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