To overcome the problems of slow convergence speed, premature convergence leading to local optimization and parameter constraints when solving high-dimensional multi-modal optimization problems, an adaptive dynamic disturbance strategy for differenti...
The dual-population differential evolution (DDE) algorithm is an optimization technique that simultaneously maintains two populations to balance global and local search. It has been demonstrated to outperform single-population differential evolution...
The differential evolution (DE) algorithm is a simple and efficient population-based evolutionary algorithm. In DE, the mutation strategy and the control parameter play important roles in performance enhancement. However, single strategy and fixed pa...
Symmetry in a differential evolution (DE) transforms a solution without impacting the family of solutions. For symmetrical problems in differential equations, DE is a strong evolutionary algorithm that provides a powerful solution to resolve global o...
In the realm of precision agriculture, agricultural UAV path planning is challenging because the UAV must avoid obstacles, follow uneven terrain, and satisfy multiple flight constraints simultaneously. Differential evolution (DE) has been widely adop...
This paper presents an innovative optimization algorithm based on differential evolution that combines advanced mutation techniques with intelligent termination mechanisms. The proposed algorithm is designed to address the main limitations of classic...
At the Second Water Intake Pump Station of the Chenhang Reservoir in Shanghai, suboptimal pump scheduling resulted in electricity consumption cost attributable to pump-motor equipment accounting for an exceptionally large proportion of the total powe...
Improving absolute accuracy in industrial manipulators remains difficult because rigid-body kinematic calibration cannot fully represent configuration-dependent non-geometric effects. Drawing inspiration from biological brain–body co-adaptation...
For numerous years, researchers have extensively explored real parameter single-objective optimization by evolutionary computation. Among the various types of evolutionary algorithms, Differential Evolution (DE) performs outstandingly. Recently, the...
For most of differential evolution (DE) algorithm variants, premature convergence is still challenging. The main reason is that the exploration and exploitation are highly coupled in the existing works. To address this problem, we present a novel DE...
Differential evolution is an evolutionary algorithm that is used to solve complex numerical optimization problems. Differential evolution balances exploration and exploitation to find the best genes for the objective function. However, finding this b...
Differential wavefront sensing (DWS) is widely adopted for high-precision angular detection in interferometric systems, yet its measurement range is constrained by the nonlinear implicit phase–angle relationship. This paper proposes a strategy-...
Differential Evolution (DE) has been extensively adopted for multi-objective optimization due to its efficient and straightforward framework. In DE, the mutation operator influences the evolution of the population. In this paper, an adaptive Grid-bas...
This paper presents an energy-saving strategy that was applied to a parallel variable frequency pump system of a water circulation pumping station. Firstly, the mathematical model of shaft power consumption for the parallel pump system was establishe...
The Interior layout model is to optimize the arrangement position of each room to maximize the comfort and quality of life of residents. Due to the complexity of the Interior layout problem, the computation of fitness function costs lots of time. To...
A parallel compact Differential Evolution (pcDE) algorithm is proposed in this paper. The population is separated into multiple groups and the individual is run by using the method of compact Differential Evolution. The communication is implemented a...
Numerical optimization has been a popular research topic within various engineering applications, where differential evolution (DE) is one of the most extensively applied methods. However, it is difficult to choose appropriate control parameters and...
As traditional power grids are unable to meet growing demand, extensive research on multi-microgrid scheduling has begun to address the issues present in conventional power grids. However, existing studies on the scheduling of grid-connected multi-mi...
An edge-weighted consensus-based formation control strategy is presented for mobile robots. In the edge-weighted strategy, a desired formation pattern is achieved by adjusting gain weights related to the distance between robots. Moreover, the edge-we...
Differential evolution (DE) is a very effective stochastic optimization algorithm based on population for solving various real-world problems. The quality of solutions to these problems is mainly determined by the combination of mutation strategies a...
Unmanned Aerial Vehicle Path Planning (UAVPP) in obstacle-rich environments requires trajectories that are collision-free, threat-aware, and feasible under practical flight constraints. This study proposes an Intelligent Cooperative Differential Evol...
To overcome the shortcomings of the harmony search algorithm, such as its slow convergence rate and poor global search ability, a reward population-based differential genetic harmony search algorithm is proposed. In this algorithm, a population is di...
The differential evolution (DE) algorithm is a popular and efficient evolutionary algorithm that can be used for single objective real-parameter optimization. Its performance is greatly affected by its parameters. Generally, parameter control strateg...
Quadrupole mass spectrometers are highly sensitive and specific analytical instruments, widely used in pharmaceuticals, clinical diagnostics, and other fields. Their performance depends on a tuning process to optimize key parameters, which has tradit...
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...
The Wind Farm Layout Optimization Problem (WFLOP) aims to improve wind energy utilization and reduce wake-induced power losses through optimal placement of wind turbines. Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been widely...
To address the limitations of mobile robots in path planning within farmland-specific environments, this paper proposes a biomimetic model: Multi-strategy Collaborative Evolution Honey Badger Algorithm (MCEHBA), MCEHBA achieves improvements through t...
Path planning and collision avoidance issues are key to the autonomous navigation of unmanned surface vehicles (USVs). This study proposes an adaptive differential evolution algorithm model integrated with the analytic hierarchy process (AHP-ADE). Th...
Industrial control systems (ICS) are facing increasing cybersecurity issues, leading to enormous threats and risks to numerous industrial infrastructures. In order to resist such threats and risks, it is particularly important to scientifically const...
Differential evolution (DE) is a simple, effective, and robust algorithm, which has demonstrated excellent performance in dealing with global optimization problems. However, different search strategies are designed for different fitness landscape con...
With the increasing complexity of engineering optimization and intelligent decision-making problems, traditional metaheuristic algorithms often suffer from premature convergence, loss of population diversity, and insufficient adaptability to complex...
Photovoltaic (PV) systems are crucial for converting solar energy into electricity. Optimization, control, and simulation for PV systems are important for effectively harnessing solar energy. The exactitude of associated model parameters is an import...
In this study, an enhanced variant of the Secretary Bird Optimization Algorithm (SBOA), named MSVSBOA, is proposed to address the limitations of the SBOA in global optimization and UAV 3D path-planning. The proposed MSVSBOA integrates three complemen...
With the increase of on-orbit maintenance and support requirements, the application of a space manipulator is becoming more promising. In actual operation, the strong coupling of the free-floating space robot itself and the unknown disturbance of the...
To cope with common local optimum traps and balance exploration and development in complex multi-peak optimisation problems, this paper puts forth a Dual-Performance Multi-subpopulation Adaptive Restart Differential Evolutionary Algorithm (DPR-MGDE)...
Agricultural sectors all over the world are facing water deficiencies as a result of various factors. Countries in the Greater Mekong Subregion (GMS) in particular depend on the production of agricultural products; thus, drought has become a critical...
In the past two decades, China’s manufacturing industry has achieved great success. However, pollution and environmental impacts have become more serious while this industry has grown. The economic and emission dispatch (EED) problem is a typical mul...
This paper introduces TRIDENT-DE, a novel ensemble-based variant of Differential Evolution (DE) designed to tackle complex continuous global optimization problems. The algorithm leverages three complementary trial vector generation strategies best/1/...
This paper introduces a variant of differential evolution with micro-populations, called
Understanding the evolution of farmers’ livelihood strategies (FLS) is crucial for maintaining the sustainable development of farmers’ livelihoods, especially on the arid Loess Plateau, China. This study constructed a novel analytical fra...
Multimodal optimization is characterized by the dual imperative of achieving global peak diversity and local precision enhancement for discovered solutions. An adaptive memetic differential evolution is proposed in this work based on the virtual popu...
Environmental concerns and rising energy prices put great pressure on the manufacturing industry to reduce pollution and save energy. Electricity is one of the main machinery energy sources in a plant; thus, reducing energy consumption both saves ene...
Under the too short arc scenario, the evolutionary-based algorithm has more potential than traditional methods in initial orbit determination. However, the underlying multimodal phenomenon in initial orbit determination is ignored by current works. I...
This research aims to develop ensemble machine-learning methods for forecasting the ultimate tensile strength (UTS) of friction stir welding (FSW). The substance utilized in the experiment was a mixture of aluminum alloys AA5083 and AA5061. An ensemb...
This study proposes an improved vector-weighted averaging algorithm (IDEINFO) for the optimization of different problems. The original vector-weighted optimization algorithm (INFO) uses weighted averaging for entity structures and uses three core pro...
The recently proposed Animated Oat Optimization (AOO) algorithm exhibits competitive search behavior, but its fixed branching rules and limited use of inter-individual information may cause diversity loss and premature stagnation. This study proposes...
The rapid development of intelligent systems has introduced increasingly sophisticated optimization problems across diverse domains. While contemporary metaheuristic algorithms, including the recent Artificial Lemming Algorithm (ALA), have shown cons...
Differential evolution (DE) is one of the most popular and widely used optimizers among the community of evolutionary computation. Despite numerous works having been conducted on the improvement of DE performance, there are still some defects, such a...
The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency and mitigate energy losses caused by wake effects by optimizing the spatial layout of wind turbines. Although Genetic Algorithms (GAs) and Particle...
The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency under different wind conditions by optimizing the spatial layout of wind turbines to fully mitigate energy losses caused by wake effects. Some high-...
of 21