ArticleBiomimetics (Basel, Switzerland)2024
Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications.
Article in Biomimetics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- A multi-strategy integrated improved nutcracker optimization algorithm and its applications in water conservancy engineering.iScience · 2026Article
- Research on UAV Path Planning Based on Enhanced Artificial Lemming Algorithm.Biomimetics (Basel, Switzerland) · 2026Article
- Aerodynamic Optimization of Relay Nozzle Using a Chebyshev KAN Surrogate Model Integration and an Improved Multi-Objective Red-Billed Blue Magpie Optimizer.Biomimetics (Basel, Switzerland) · 2026Article
- An Improved Genghis Khan Shark Optimization Algorithm for Solving Optimization Problems.Biomimetics (Basel, Switzerland) · 2026Article
- A modified Dung Beetle optimizer for combined heat and power economic dispatch considering power losses, valve-point effects, and operating constraints.Scientific reports · 2026Article
- Optimizing accuracy and dimensionality: a swarm intelligence strategy for robust cancer genomics classification.BioData mining · 2025Article
- An Improved Crested Porcupine Optimization Algorithm Incorporating Butterfly Search and Triangular Walk Strategies.Biomimetics (Basel, Switzerland) · 2025Article
- Multi-Strategy Improved Red-Billed Blue Magpie Optimization Algorithm and Its Applications.Biomimetics (Basel, Switzerland) · 2025Article
- Ship Rolling Bearing Fault Identification Under Complex Operating Conditions: Multi-Domain Feature Extraction-Based LCM-HO Enhanced LSSVM Approach.Sensors (Basel, Switzerland) · 2025Article
- Adaptive Differentiated Parrot Optimization: A Multi-Strategy Enhanced Algorithm for Global Optimization with Wind Power Forecasting Applications.Biomimetics (Basel, Switzerland) · 2025Article
- Application of an improved pelican optimization algorithm based on comprehensive strategy in PV parameter identification.Scientific reports · 2025Article
- Explainable artificial intelligence with temporal convolutional networks for adverse weather condition detection in driverless vehicles.Scientific reports · 2025Article
- A Wireless Sensor Network-Based Combustible Gas Detection System Using PSO-DBO-Optimized BP Neural Network.Sensors (Basel, Switzerland) · 2025Article
- Dynamic gold rush optimizer: fusing worker adaptation and salp navigation mechanism for enhanced search.Scientific reports · 2025Article
- An enhanced dung beetle optimizer with multiple strategies for robot path planning.Scientific reports · 2025Article
- Energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm optimization.PloS one · 2025Article
- An Advanced Whale Optimization Algorithm for Grayscale Image Enhancement.Biomimetics (Basel, Switzerland) · 2024Article
- Research on Move-to-Escape Enhanced Dung Beetle Optimization and Its Applications.Biomimetics (Basel, Switzerland) · 2024Article
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5 authors.
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Abstract
The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, and unsatisfactory convergence speed when facing complex optimization problems. In response, this paper proposes the multi-strategy improved dung beetle optimization algorithm (MDBO). The core improvements include using Latin hypercube sampling for better population initialization and the introduction of a novel differential variation strategy, termed "Mean Differential Variation", to enhance the algorithm's ability to evade local optima. Moreover, a strategy combining lens imaging reverse learning and dimension-by-dimension optimization was proposed and applied to the current optimal solution. Through comprehensive performance testing on standard benchmark functions from CEC2017 and CEC2020, MDBO demonstrates superior performance in terms of optimization accuracy, stability, and convergence speed compared with other classical metaheuristic optimization algorithms. Additionally, the efficacy of MDBO in addressing complex real-world engineering problems is validated through three representative engineering application scenarios namely extension/compression spring design problems, reducer design problems, and welded beam design problems.
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