TDOA/AOA Hybrid Localization Based on Improved Dandelion Optimization Algorithm for Mobile Location Estimation Under NLOS Simulation Environment

Chen, Haishao; Cao, Li; Yue, Yinggao

doi:10.1007/s11277-023-10578-y

Cited by 14 publications

(4 citation statements)

References 42 publications

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“…However, these advancements in IBAS come at the cost of increased computational complexity, attributable to the inclusion of these additional mechanisms. Kou et al introduced an innovative optimization algorithm-the Random Walk Simulated Annealing Variable Step Beetle Antennae Search Algorithm (RWSAVSBAS) (Zhang et al 2020c), amalgamating the exploratory behaviors of the Wolf Pack Algorithm (WPA) (Chen et al 2023) with simulated annealing and BAS. Drawing inspiration from WPA's stochastic roaming patterns, RWSAVSBAS augments BAS by incorporating 'random antennae' alongside the beetle's existing antennae, facilitating a tripartite antennae search.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

Chen,

Cao,

Chen

et al. 2024

Artif Intell Rev

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In recent years, swarm intelligence optimization algorithms have been proven to have significant effects in solving combinatorial optimization problems. Introducing the concept of evolutionary computing, which is currently a hot research topic, into swarm intelligence optimization algorithms to form novel swarm intelligence optimization algorithms has proposed a new research direction for better solving combinatorial optimization problems. The longhorn beetle whisker search algorithm is an emerging heuristic algorithm, which originates from the simulation of longhorn beetle foraging behavior. This algorithm simulates the touch strategy required by longhorn beetles during foraging, and achieves efficient search in complex problem spaces through bioheuristic methods. This article reviews the research progress on the search algorithm for longhorn beetles from 2017 to present. Firstly, the basic principle and model structure of the beetle whisker search algorithm were introduced, and its differences and connections with other heuristic algorithms were analyzed. Secondly, this paper summarizes the research achievements of scholars in recent years on the improvement of longhorn whisker search algorithms. Then, the application of the beetle whisker search algorithm in various fields was explored, including function optimization, engineering design, and path planning. Finally, this paper summarizes the research achievements of scholars in recent years on the improvement of the longhorn whisker search algorithm, and proposes future research directions, including algorithm deep learning fusion, processing of multimodal problems, etc. Through this review, readers will have a comprehensive understanding of the research status and prospects of the longhorn whisker search algorithm, providing useful guidance for its application in practical problems.

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Section: Simulated Annealing Algorithmmentioning

confidence: 99%

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

Chen,

Cao,

Chen

et al. 2024

Artif Intell Rev

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“…Among them, parameter r i is a random number between (0, 1) which obeys normal distribution, U B is the maximum value in decision space, L B is the minimum value in decision space, N is the maximum row value of the population matrix, d is the maximum column value of the population matrix, and the same characters in the following are synonymous [34].…”

Section: Dandelion Optimization Algorithmmentioning

confidence: 99%

Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm

Tang,

Cao,

Chen

et al. 2024

Biomimetics

Self Cite

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In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, which is based on the problems of slow optimization speed and being easily susceptible to falling into local extremum in the optimization ability of the dandelion optimization algorithm. This hybrid algorithm makes the whole algorithm more diverse by introducing the strong global search ability of particle swarm optimization and the unique individual update rules of the dandelion algorithm (i.e., rising, falling and landing). The ascending and descending stages of dandelion also help to introduce more changes and explorations into the search space, thus better balancing the global and local search. The experimental results show that compared with other algorithms, the proposed PSODO algorithm greatly improves the global optimal value search ability, convergence speed and optimization speed. The effectiveness and feasibility of the PSODO algorithm are verified by solving 22 benchmark functions and three engineering design problems with different complexities in CEC 2005 and comparing it with other optimization algorithms.

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“…To address these challenges, researchers have developed wireless sensor network localization algorithms using particle swarm optimization (PSO) to reduce the biases typical of traditional methods [30][31][32]. Additionally, Chen et al introduced an advanced TDOA/AOA localization method utilizing the dandelion optimization algorithm, which enhances optimization performance by integrating optimal solutions from two populations through a multi-objective mechanism [33]. Nevertheless, as TDOA and AOA errors increase, the accuracy of the final convergence results deteriorates.…”

Section: Introductionmentioning

confidence: 99%

Development of Hydroacoustic Localization Algorithms for AUV Based on the Error-Corrected WMChan-Taylor Algorithm

Yang,

Gao,

et al. 2024

JMSE

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Autonomous underwater vehicles (AUVs) are susceptible to non-line-of-sight (NLOS) errors and noise bias at receiving stations during the application of hydroacoustic localization systems, leading to a degradation in positioning accuracy. To address this problem, this paper optimizes the Chan-Taylor algorithm. Initially, we propose the Weighted Modified Chan-Taylor (WMChan-Talor) algorithm, which introduces dynamic weights into the Chan algorithm to correct noise variance at measurement stations, thereby improving the accuracy of AUV positioning. Computer simulations validate the effectiveness of the WMChan-Taylor algorithm in enhancing positioning accuracy. To further address the accuracy degradation caused by noise deviations across different receiving stations, we introduce an error-corrected WMChan-Taylor algorithm. This algorithm utilizes a standard residual function to eliminate significant delays caused by large errors at receiving stations and applies standard residual weighting to improve the combined positioning solution. The performance of the error-corrected WMChan-Taylor algorithm is demonstrated through both computer and semi-physical simulation experiments, confirming its capability to isolate noisier stations and thus enhance overall positioning accuracy.

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TDOA/AOA Hybrid Localization Based on Improved Dandelion Optimization Algorithm for Mobile Location Estimation Under NLOS Simulation Environment

Cited by 14 publications

References 42 publications

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm

Development of Hydroacoustic Localization Algorithms for AUV Based on the Error-Corrected WMChan-Taylor Algorithm

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