Enhanced Dung Beetle Optimization Algorithm for Practical Engineering Optimization

TL;DR: This paper proposes a hybrid Dung Beetle Optimization (DBO) algorithm, SRBDBO, combining Beluga Whale Optimization and somersault foraging, to improve global exploration and diversity, using three strategies: interaction behavior, adaptive somersault factor, and refraction opposition-based learning.

TL;DR: DHTROA is an improved Tyrannosaurus optimization algorithm with hybrid topology structure that enhances convergence speed, optimality search accuracy, global search ability, and stability.

TL;DR: The differential amplifier circuit with current mirror load is optimized through the application of the Dung Beetle Optimization (DBO) algorithm, achieving with the least amount of transistor area and power dissipation when compared to the results of the Seeker Optimization Algorithm (SOA), Opposition based Harmony Search Algorithm (OHS), craziness-based particle swarm optimization (CRPSO), and Cuckoo Search algorithms.

Abstract: Rapid and accurate prediction of the maximum remaining life of lithium-ion batteries is a critical technical challenge for enhancing battery management system reliability and enabling the efficient secondary utilization of retired batteries. Traditional approaches that rely on full charge–discharge cycles or complex electrochemical models often suffer from long detection time and limited adaptability, making them unsuitable for fast testing scenarios. To address these limitations, this study proposes a novel capacity prediction method that integrates charging segment feature extraction with a back-propagation neural network (BPNN) co-optimized using the genetic algorithm (GA) and dung beetle optimizer (DBO). Leveraging the public CALCE datasets, key degradation-related features were extracted from partial charging segments to serve as inputs to the prediction framework. The hybrid GA_DBO algorithm is employed to jointly optimize the BPNN’s weights, learning rate, and activation thresholds. A comparative analysis is conducted across various charging durations (900 s, 1800 s, and 2700 s) to evaluate performance under different input lengths. Results reveal that the model using 1800 s charging segment features achieves the best overall accuracy, with a test set mean squared error (MSE) of 0.0001 Ah2, mean absolute error (MAE) of 0.0092 Ah, root mean square error (RMSE) of 0.0122 Ah, and a coefficient of determination (R2) of 99.66%, demonstrating strong robustness and predictive capability. This research overcomes the traditional reliance on full cycles, demonstrating the effectiveness of short charging segments combined with intelligent optimization algorithms. The proposed method offers a high-precision, low-cost solution for online battery health monitoring and rapid sorting of retired batteries, highlighting its significant engineering application potential.

TL;DR: The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces.

TL;DR: A new nature-inspired algorithm, namely butterfly optimization algorithm (BOA) that mimics food search and mating behavior of butterflies, to solve global optimization problems and results indicate that the proposed BOA is more efficient than other metaheuristic algorithms.

TL;DR: A novel population-based technique called dung beetle optimizer (DBO) algorithm is presented, which is inspired by the ball-rolling, dancing, foraging, stealing, and reproduction behaviors of dung beetles, and proves the superiority of the DBO algorithm against other currently popular optimization techniques.

TL;DR: A review of swarm intelligence algorithms can be found in this paper, where the authors highlight the functions and strengths from 127 research literatures and briefly provide the description of their successful applications in optimization problems of engineering fields.