ArticleScientific reports2024
A hybrid particle swarm optimization algorithm for solving engineering problem.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.
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12 citing papers in PubMed.
- An improved human memory algorithm with multi-directional and chaotic approaches for global optimization and energy-efficient cluster head selection in WSNs.Scientific reports · 2026Article
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- Adaptive multi mechanism integration in the crested porcupine optimizer for global optimization and engineering design problems.Scientific reports · 2026Article
- Pediatric diabetes prediction using machine learning.Scientific reports · 2026Article
- CNN-MLP framework for forest burned areas prediction using PSO-WOA algorithm.Scientific reports · 2026Article
- Hybrid Bacterial Colony Optimization and Particle Swarm Optimization for load balancing in fog computing.PloS one · 2026Article
- Research on establishment decision of medical equipment measurement standard based on GDM-AHP.Scientific reports · 2025Article
- A contribution-interpretable belief rule base model for obesity risk prediction using physical indicators and dietary habits.Frontiers in endocrinology · 2025Article
- Harmonic oscillator based particle swarm optimization.PloS one · 2025Article
- Modulation optimization method for seven-level SHEPWM inverter based on EPSO algorithm.Scientific reports · 2024Article
- Cooperative metaheuristic algorithm for global optimization and engineering problems inspired by heterosis theory.Scientific reports · 2024Article
- MSPO: A machine learning hyperparameter optimization method for enhanced breast cancer image classification.Digital healthArticle
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
To overcome the disadvantages of premature convergence and easy trapping into local optimum solutions, this paper proposes an improved particle swarm optimization algorithm (named NDWPSO algorithm) based on multiple hybrid strategies. Firstly, the elite opposition-based learning method is utilized to initialize the particle position matrix. Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the whale optimization algorithm (WOA) and the Differential Evolution (DE) mutation strategy in the later iteration to accelerate the convergence speed. The NDWPSO is further compared with other 8 well-known nature-inspired algorithms (3 PSO variants and 5 other intelligent algorithms) on 23 benchmark test functions and three practical engineering problems. Simulation results prove that the NDWPSO algorithm obtains better results for all 49 sets of data than the other 3 PSO variants. Compared with 5 other intelligent algorithms, the NDWPSO obtains 69.2%, 84.6%, and 84.6% of the best results for the benchmark function (
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