Somersault Foraging and Elite Opposition-Based Learning Dung Beetle Optimization Algorithm

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(19), P. 8624 - 8624

Published: Sept. 25, 2024

To tackle the shortcomings of Dung Beetle Optimization (DBO) Algorithm, which include slow convergence speed, an imbalance between exploration and exploitation, susceptibility to local optima, a Somersault Foraging Elite Opposition-Based Learning (SFEDBO) Algorithm is proposed. This algorithm utilizes elite opposition-based learning strategy as method for generating initial population, resulting in more diverse population. address exploitation algorithm, adaptive employed dynamically adjust number dung beetles eggs with each iteration Inspired by Manta Ray (MRFO) we utilize its somersault foraging perturb position optimal individual, thereby enhancing algorithm’s ability escape from optima. verify effectiveness proposed improvements, SFEDBO utilized optimize 23 benchmark test functions. The results show that achieves better solution accuracy stability, outperforming DBO terms optimization on Finally, was applied practical application problems pressure vessel design, tension/extension spring 3D unmanned aerial vehicle (UAV) path planning, were obtained. research shows this paper applicable actual has performance.

Language: Английский

Enhanced snow ablation optimizer using advanced quadratic and newton interpolation with taylor neighbourhood search and second-order differential perturbation strategies for high-dimensional feature selection

Evolutionary Intelligence, Journal Year: 2025, Volume and Issue: 18(2)

Published: March 27, 2025

Language: Английский

Enhanced artificial hummingbird algorithm with chaotic traversal flight

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 29, 2024

Tackling the shortcomings of slow convergence, imprecision, and entrapment in local optima inherent traditional meta-heuristic algorithms, this study presents enhanced artificial hummingbird algorithm with chaotic traversal flight (CEAHA), which employs ergodicity within foundational framework conventional algorithm. This approach implements motion regions solution space, ensuring a thorough exploration potential preventing algorithmic stagnation at maxima by guaranteeing non-repetitive all search states. also analyzes intrinsic mechanisms eight different mappings affect optimization performance, from perspectives invariant measures efficiency ergodic motion. In comparative tests 21 algorithms on CEC2014, CEC2019, CEC2022 benchmark suites across various dimensions, CEAHA demonstrates superior performance. Furthermore, practicability robustness have been confirmed mechanical design problems through 4 engineering instances: pressure vessel, gear trains, speed reducers, piston levers.

Language: Английский