Background: Chest X-ray imaging is a widely used diagnostic modality for identifying various lung diseases. Accurate multiclass classification of lung diseases enables timely treatment and improves patient survival. However, disease detection using c...
The efficiency in the controller performance of a BLDC motor in an uncertain environment highly depends on the adaptability of the controller gains. In this paper, the chaotic adaptive tuning strategy for controller gains (CATSCG) is proposed for the...
Forecasting agricultural futures prices across multiple commodities remains highly challenging due to nonlinear price dynamics, strong market volatility, and complex cross-market and cross-commodity interactions. To address these issues, this paper p...
The execution time that takes to perform numerical simulation of a chaotic oscillator mainly depends on the time-step h. This paper shows that the optimization of chaotic oscillators can be enhanced by estimating the highest h in either one-step or m...
Since its original publication in 1978, Lozi’s chaotic map has been thoroughly explored and continues to be. Hundreds of publications have analyzed its particular structure and applied its properties in many fields (e.g., improvement of physica...
To address the challenges of slow convergence speed, poor convergence precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a path planning method based on an Improved Huma...
In order to obtain the optimal cladding process parameters for repairing the inner wall of the cylinder, 316L stainless steel powder was laser clad onto 27SiMn steel, which is the base material of the inner wall of the cylinder. The CCD (Central Comb...
To solve the limitations of single optimization algorithms, such as premature convergence, insufficient global exploration, and high susceptibility to local optima, a Hybrid Optimization Algorithm (HOA) based on multi-subpopulation collaboration and...
The economic emission dispatch problem (EEDP) is a nonconvex and nonsmooth multiobjective optimization problem in the power system field. Generally, fuel cost and total emissions of harmful gases are the problem objective functions. The EEDP decision...
In CMOS integrated circuit (IC) design, operational amplifiers are one of the most useful active devices to enhance applications in analog signal processing, signal conditioning and so on. However, due to the CMOS technology downscaling, along the ve...
In recent years, remote sensing data have gradually shown a trend towards big data. The cost of the efficient storage, transmission, and processing of multi-source massive remote sensing data is very high. In practical applications, it is usually nec...
The traditional particle swarm optimization (PSO) algorithm often exhibits defects such as of slow convergence and easily falling into a local optimum. To overcome these problems, this paper proposes an enhanced variant featuring adaptive selection....
In this paper, a hybrid machine learning approach is proposed for accurate prediction of heart disease. The features are categorized into numerical and categorical types, which are extracted independently by a deep neural network (DNN) and CatBoost,...
The main goal of this work is to optimize the chaotic behavior of a three-dimensional chaotic-spherical-attractor-generating fractional-order system and compare the results with its novel hyperchaotic counterpart. The fractional-order chaotic system...
This paper presents the application of three optimization algorithms to increase the chaotic behavior of the fractional order chaotic Chen system. This is achieved by optimizing the maximum Lyapunov exponent (MLE). The applied optimization techniques...
When a new chaotic oscillator is introduced, it must accomplish characteristics like guaranteeing the existence of a positive Lyapunov exponent and a high Kaplan–Yorke dimension. In some cases, the coefficients of a mathematical model can be va...
Traditional path planning algorithms typically focus only on path length, which fails to meet the low energy consumption requirements for wall-climbing robots in bridge inspection. This paper proposes an improved sparrow search algorithm based on log...
Offset boosting enables spatial regulation of chaotic attractors, yet interactions among jointly regulated states and their computational value remain insufficiently explored. To connect correlated state regulation with intelligent search, this study...
Inspired by the collective behavior of fish schools, the fish school search (FSS) algorithm is a technique for finding globally optimal solutions. The algorithm is characterized by its simplicity and high performance; FSS is computationally inexpensi...
The Jaya optimization algorithm is a simple, fast, robust, and powerful population-based stochastic metaheuristic that in recent years has been successfully applied in a variety of global optimization problems in various application fields. The essen...
Lilies, a key cash crop in Lanzhou, China, widely planted in coal-based fields, cultivated fields, and gardens, face significant yield and quality reduction due to weed infestation, which competes for essential nutrients, water, and light. To address...
Unmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more...
Aiming at the problems of the basic sparrow search algorithm (SSA) in terms of slow convergence speed and the ease of falling into the local optimum, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are i...
In modern power-system load frequency control (LFC), proportional–integral–derivative (PID) controllers are widely used because of their simple structure and ease of implementation. However, the combined effects of communication delay and...
To improve the safety of road transportation of Spent Nuclear Fuel (SNF), this paper proposes a novel approach for risk identification and chaotic synchronous control in SNF road transportation systems. Firstly, a dynamic risk evolution model for the...
To address the drawbacks of the traditional snake optimization method, such as a random population initialization, slow convergence speed, and low accuracy, an adaptive t-distribution mixed mutation snake optimization strategy is proposed. Initially,...
Metaheuristic optimizers often suffer from premature convergence, diversity loss, and stagnation in complex search landscapes. This study proposes HCQ-SGuA, a plant-growth-inspired framework that strengthens the Sowing phase through structured popula...
The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with...
The Ivy Optimization Algorithm (IVY) is a novel swarm intelligence optimization algorithm that simulates the phototropic growth mechanism of plants. To comprehensively improve the overall optimization performance, this paper proposes an enhanced Ivy...
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...
Recurrence analysis is a powerful tool for revealing the nonlinear dynamic characteristics of complex systems and identifying the wear states of friction systems. To investigate the influence of multiple factors—including sampling length, signa...
To address node redundancy and coverage holes in Wireless Sensor Network (WSN) deployment, this paper proposes an Improved Northern Goshawk Optimization (INGO) algorithm with multiple enhancements. It integrates a Diverse Chaotic Map Initialization S...
The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety perf...
Forecasting and modeling time series is a crucial aspect of economic research for academics and business practitioners. The ability to predict the direction of stock prices is vital for creating an investment plan or determining the optimal time to m...
The rapid and accurate identification of photovoltaic (PV) model parameters is of great significance in solving practical engineering problems such as PV power prediction, maximum power point tracking and battery failure model recognition. Aiming at...
Improvements to bio-inspired optimisers usually answer generic defects with generic operators. This paper instruments the red-billed blue magpie optimizer (RBMO) and measures what fails. Both operators scale their step by differences between populati...
Trajectory optimization of manipulators is crucial for achieving efficient, low-energy-consumption, and stable operation. The standard Chicken Swarm Optimization (CSO) algorithm and its variants tend to fall into local optima during optimization, mak...
To address the limitations of traditional image encryption algorithms in key optimization and encryption quality assessment, in this paper we propose a framework for image encryption based on surrogate-assisted differential evolution. First, we const...
Aiming at the problem that the aggravation of the wheel tread wear of high-speed trains leads to the deterioration of train operation performance and an increase in re-profiling times, a multi-objective data-driven optimization design method for the...
To achieve an optimal trade-off between production efficiency and energy benefits in complex manufacturing environments, this paper addresses the Green Flexible Job Shop Scheduling Problem (GFJSP) by establishing a multi-objective mathematical model...
The black widow spider optimization algorithm (BWOA) had the problems of slow convergence speed and easily to falling into local optimum mode. To address these problems, this paper proposes a multi-strategy black widow spider optimization algorithm (...
The bamboo forest growth optimization (BFGO) algorithm combines the characteristics of the bamboo forest growth process with the optimization course of the algorithm. The algorithm performs well in dealing with optimization problems, but its exploita...
This study presents an empirical comparison of the standard differential evolution (DE) against three random sampling methods to solve robust optimization over time problems with a survival time approach to analyze its viability and performance capac...
In order to solve problems with the original crayfish optimization algorithm (COA), such as reduced diversity, local optimization, and insufficient convergence accuracy, a multi-strategy optimization algorithm for crayfish based on differential evolu...
To address the issues of large overshoot, slow response, poor stability, and suboptimal control performance of traditional PID algorithms caused by the nonlinear relationship between the rotational speed and output flow rate of micro screw pump motor...
This paper proposes an improved Harris Hawks Optimization (IHHO) algorithm to enhance maximum power point tracking (MPPT) performance in photovoltaic (PV) systems operating under various conditions. The IHHO introduces Tent chaotic mapping to improve...
An adaptive learning pigeon-inspired optimization based on mutation disturbance (ALPIO) is proposed for solving the problems of fuel consumption and threat avoidance in spacecraft cluster orbit reconstruction. First, considering the constraints of ma...
The multi-objective optimization problem is difficult to solve with conventional optimization methods and algorithms because there are conflicts among several optimization objectives and functions. Through the efforts of researchers and experts from...
Aiming at the problem of the low collaborative efficiency of outsourced processing of coating machine oven parts under the network collaborative manufacturing mode, this paper proposes a composition optimization method for coating machine oven-manufa...
Cold-atom absolute gravimeters are widely used for measuring the acceleration of gravity, yet their sensitivity is often limited by ground vibrations. Existing vibration compensation algorithms struggle to strike a balance between search accuracy and...
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