An improved atomic search algorithm for optimization and application in ML DOA estimation of vector hydrophone array
2
TL;DR: An improved atomic search optimization (IASO) algorithm is proposed based on the idea of speed update in particle swarm optimization (PSO), which holds great potential for not only guaranteeing the estimation accuracy but also greatly reducing the computational complexity of multidimensional nonlinear optimization of ML estimator.
read more
Abstract: The atom search optimization (ASO) algorithm has the characteristics of fewer parameters and better performance than the traditional intelligent optimization algorithms, but it is found that ASO may easily fall into local optimum and its accuracy is not higher. Therefore, based on the idea of speed update in particle swarm optimization (PSO), an improved atomic search optimization (IASO) algorithm is proposed in this paper. Compared with traditional ASO, IASO has a faster convergence speed and higher precision for 23 benchmark functions. IASO algorithm has been successfully applied to maximum likelihood (ML) estimator for the direction of arrival (DOA), under the conditions of the different number of signal sources, different signal-to-noise ratio (SNR) and different population size, the simulation results show that ML estimator with IASO algorithum has faster convergence speed, fewer iterations and lower root mean square error (RMSE) than ML estimator with ASO, sine cosine algorithm (SCA), genetic algorithm (GA) and particle swarm optimization (PSO). Therefore, the proposed algorithm holds great potential for not only guaranteeing the estimation accuracy but also greatly reducing the computational complexity of multidimensional nonlinear optimization of ML estimator.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
BU-DLNet: Breast Ultrasonography-Based Cancer Detection Using Deep-Learning Network Selection and Feature Optimization
TL;DR: In this article , the authors proposed a BU image-based framework for the diagnosis of breast cancer in women using wrapper-based optimization algorithms, including the marine predator algorithm, generalized normal distribution optimization, slime mold algorithm, equilibrium optimizer (EO), manta-ray foraging optimization, atom search optimization, Harris hawks optimization, Henry gas solubility optimization, path finder algorithm, and poor and rich optimization, were employed to compute the optimal subset of deep features using a support vector machine classifier.
2
DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning
Wang Hong-yan,Yanping Bai,Jing Ren,Peng Wang,Ting Xu,Wendong Zhang,Guojun Zhang +6 more
TL;DR: This paper proposes Vector-SBL, a DOA estimation method for vector hydrophones, leveraging sparse Bayesian learning to accurately reconstruct multidimensional sound field information and achieve precise DOA estimation for multiple sources with low SNR and limited snapshots.
References
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
46.9K
Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes
TL;DR: In this paper, a numerical algorithm integrating the 3N Cartesian equations of motion of a system of N points subject to holonomic constraints is formulated, and the relations of constraint remain perfectly fulfilled at each step of the trajectory despite the approximate character of numerical integration.
20.9K
SCA: A Sine Cosine Algorithm for solving optimization problems
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.
4.6K
Maximum likelihood localization of multiple sources by alternating projection
I. Ziskind,Mati Wax +1 more
TL;DR: An algorithm, referred to as APM, for computing the maximum-likelihood estimator of the locations of simple sources in passive sensor arrays is presented and the convergence of the algorithm to the global maximum is demonstrated for a variety of scenarios.
1.4K
A novel nature-inspired algorithm for optimization: Squirrel search algorithm
TL;DR: This optimizer imitates the dynamic foraging behaviour of southern flying squirrels and their efficient way of locomotion known as gliding and provides more accurate solutions with high convergence rate as compared to other existing optimizers.
874