Particle swarm optimization (PSO) has the disadvantages of easily getting trapped in local optima and a low search accuracy. Scores of approaches have been used to improve the diversity, search accuracy, and results of PSO, but the balance between exploration and exploitation remains sub-optimal. Many scholars have divided the population into multiple sub-populations with the aim of managing it in space. In this paper, a multi-stage search strategy that is dominated by mutual repulsion among particles and supplemented by attraction was proposed to control the traits of the population. From the angle of iteration time, the algorithm was able to adequately enhance the entropy of the population under the premise of satisfying the convergence, creating a more balanced search process. The study acquired satisfactory results from the CEC2017 test function by improving the standard PSO and improved PSO.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8472527 | PMC |
| http://dx.doi.org/10.3390/e23091200 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Swarming bacteria exhibit developmental phase transitions to establish scattered colonies in new regions.
ISME J
January 2025
Center for Fundamental and Applied Microbiomics, Biodesign Institue, Arizona State University, Tempe, AZ 85287.
The collective surface motility and swarming behavior of microbes play a crucial role in the formation of polymicrobial communities, shaping ecosystems as diverse as animal and human microbiota, plant rhizospheres, and various aquatic environments. In the human oral microbiota, T9SS-driven gliding bacteria transport non-motile microbes and bacteriophages as cargo, thereby influencing the spatial organization and structural complexity of these polymicrobial communities. However, the physical rules governing the dispersal of T9SS-driven bacterial swarms are barely understood.
View Article and Find Full Text PDFSpectral optimization of supercontinuum shaping using metaheuristic algorithms, a comparative study.
Sci Rep
January 2025
Photonics Laboratory, Tampere University, 33104, Tampere, Finland.
Supercontinuum generation in optical fiber involves complex nonlinear dynamics, making optimization challenging, and typically relying on trial-and-error or extensive numerical simulations. Machine learning and metaheuristic algorithms offer more efficient optimization approaches. We report here an experimental study of supercontinuum spectral shaping by tuning the phase of the input pulses, different optimization approaches including a genetic algorithm, particle swarm optimizer, and simulated annealing.
View Article and Find Full Text PDFEfficient control strategy for electric furnace temperature regulation using quadratic interpolation optimization.
Sci Rep
January 2025
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, Kyiv, 03680, Ukraine.
Electric furnaces play an important role in many industrial processes where precise temperature control is essential to ensure production efficiency and product quality. Traditional proportional-integral-derivative (PID) controllers and their modified versions are commonly used to maintain temperature stability by reacting quickly to deviations. In this study, the real PID plus second-order derivative (RPIDD) controller is introduced for the first time for industrial temperature control applications, which is a novel alternative that has not yet been investigated in the literature.
View Article and Find Full Text PDFFault localization for automatic train operation based on the adaptive error locating array algorithm.
Sci Rep
January 2025
School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China.
When the combinatorial testing method is used to locate faults in the complex signalling system of high-speed rail in order to prevent the system from being affected by combinatorial testing case explosion, which could results from the masking effects caused by multiple faults, the Minimum Fault Schema (MFS) can be accurately and efficiently located. Taking the Automatic Train Operation (ATO) scenario in intelligent high-speed rail as an example, a fault localization method based on the Adaptive Error Locating Array (AELA) algorithm is proposed. To begin with, according to the characteristics of ATO, the adaptive fault localization model is designed and the test parameter table is constructed.
View Article and Find Full Text PDFBio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments.
Sci Rep
January 2025
Xiamen Topstar Co., Ltd., Xiamen, 361000, Fujian, China.
Automated guided vehicles play a crucial role in transportation and industrial environments. This paper presents a proposed Bio Particle Swarm Optimization (BPSO) algorithm for global path planning. The BPSO algorithm modifies the equation to update the particles' velocity using the randomly generated angles, which enhances the algorithm's searchability and avoids premature convergence.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!