Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization

Cao, Li; Chen, Haishao; Chen, Yaodan; Yue, Yinggao; Zhang, Xin

doi:10.3390/biomimetics8020186

Cited by 18 publications

(16 citation statements)

References 42 publications

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“…Many metaheuristic algorithms have recently been reported in addition to the above-discussed algorithms for numerical and real-world engineering design optimization problems, including data clustering. For instance, ant colony optimization 35 , firefly algorithm 36 , 37 , flower pollination algorithm 38 , grey wolf optimizer (GWO) 39 – 42 , Jaya algorithm 43 , Teaching–learning based optimization (TLBO) algorithm 44 , Rao algorithm 45 , political optimizer 46 , whale optimization algorithm (WOA) 47 , Moth flame algorithm (MFO) 48 , multi-verse optimizer (MVO) 49 , Salp swarm algorithm (SSA) 50 , 51 , spotted hyena optimizer 52 , butterfly optimization 53 , lion optimization 54 , fireworks algorithm 55 , Cuckoo search algorithm 56 , bat algorithm 57 , Tabu search 58 , harmony search algorithm 59 , Newton–Raphson optimizer 60 , reptile search algorithm 61 , slime mould algorithm 62 , 63 , harris hawk optimizer 64 , Chimp optimizer 65 , artificial gorilla troop optimizer 66 , atom search algorithm 67 , marine predator algorithm 68 , 69 , sand cat swarm algorithm 70 , equilibrium optimizer 71 , 72 , Henry gas solubility algorithm (HGSA) 73 , resistance–capacitance algorithm 74 , arithmetic optimization algorithm 75 , quantum-based avian navigation optimizer 76 , multi trail vector DE algorithm 10 , 77 , arithmetic optimization algorithm 78 , starling murmuration optimizer 79 , atomic orbit search (AOS) 80 , subtraction-average-based optimizer 81 , etc. are reported for solving optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Premkumar,

Sinha,

Ramasamy

et al. 2024

Sci Rep

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This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the grey wolf optimizer establishes a new standard for further study in metaheuristic clustering algorithms. The performance of the K-means clustering-based grey wolf optimizer is around 34% better than the original grey wolf optimizer algorithm for both numerical test problems and data clustering problems.

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Section: Introductionmentioning

confidence: 99%

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Premkumar,

Sinha,

Ramasamy

et al. 2024

Sci Rep

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“…Instead of relying on a centralized authority, devices in bio-inspired IoT systems interact locally and collectively contribute to the decision-making process. This distributed intelligence enhances fault tolerance, autonomy, and scalability while reducing dependence on a single point of failure [ 48 , 49 , 50 , 51 , 52 , 63 , 64 , 65 ].…”

Section: The Bio-inspired Iot Ecosystemmentioning

confidence: 99%

“…Bio-inspired algorithms often prioritize energy efficiency and resource optimization, aligning with sustainability goals. They leverage energy-efficient behaviors observed in biological systems, helping reduce energy consumption and environmental impact in IoT deployments [ 6 , 13 , 14 , 15 , 16 , 64 , 65 , 66 , 67 , 68 ].…”

Section: The Bio-inspired Iot Ecosystemmentioning

confidence: 99%

Bio-Inspired Internet of Things: Current Status, Benefits, Challenges, and Future Directions

Alabdulatif,

Thilakarathne

2023

Biomimetics

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There is no doubt that the involvement of the Internet of Things (IoT) in our daily lives has changed the way we live and interact as a global community, as IoT enables intercommunication of digital objects around us, creating a pervasive environment. As of now, this IoT is found in almost every domain that is vital for human survival, such as agriculture, medical care, transportation, the military, and so on. Day by day, various IoT solutions are introduced to the market by manufacturers towards making our life easier and more comfortable. On the other hand, even though IoT now holds a key place in our lives, the IoT ecosystem has various limitations in efficiency, scalability, and adaptability. As such, biomimicry, which involves imitating the systems found in nature within human-made systems, appeared to be a potential remedy to overcome such challenges pertaining to IoT, which can also be referred to as bio-inspired IoT. In the simplest terms, bio-inspired IoT combines nature-inspired principles and IoT to create more efficient and adaptive IoT solutions, that can overcome most of the inherent challenges pertaining to traditional IoT. It is based on the idea that nature has already solved many challenging problems and that, by studying and mimicking biological systems, we might develop better IoT systems. As of now, this concept of bio-inspired IoT is applied to various fields such as medical care, transportation, cyber-security, agriculture, and so on. However, it is noted that only a few studies have been carried out on this new concept, explaining how these bio-inspired concepts are integrated with IoT. Thus, to fill in the gap, in this study, we provide a brief review of bio-inspired IoT, highlighting how it came into play, its ecosystem, its latest status, benefits, challenges, and future directions.

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“…Classical swarm intelligence algorithms include particle swarm optimization (PSO) 13 , artificial bee colony (ABC) 14 , grey wolf optimization (GWO) 15 , harris hawk algorithm (HHO) 16 , and whale optimization algorithm (WOA) 17 . With the continuous development and update of technology, swarm intelligence algorithms have excelled in problems such as positioning computation 18 , path planning for travelers 19 , support vector machine optimization 20 , robotic route finding 21 , power system control 22 , and optimization of routing protocols for the Internet of Things (IoT) 23 .…”

Section: Introductionmentioning

confidence: 99%

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

Wang,

Cao,

Chen

et al. 2024

Sci Rep

Self Cite

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To address the issues of lacking ability, loss of population diversity, and tendency to fall into the local extreme value in the later stage of optimization searching, resulting in slow convergence and lack of exploration ability of the artificial gorilla troops optimizer algorithm (AGTO), this paper proposes a gorilla search algorithm that integrates the positive cosine and Cauchy's variance (SCAGTO). Firstly, the population is initialized using the refractive reverse learning mechanism to increase species diversity. A positive cosine strategy and nonlinearly decreasing search and weight factors are introduced into the finder position update to coordinate the global and local optimization ability of the algorithm. The follower position is updated by introducing Cauchy variation to perturb the optimal solution, thereby improving the algorithm's ability to obtain the global optimal solution. The SCAGTO algorithm is evaluated using 30 classical test functions of Test Functions 2018 in terms of convergence speed, convergence accuracy, average absolute error, and other indexes, and two engineering design optimization problems, namely, the pressure vessel optimization design problem and the welded beam design problem, are introduced for verification. The experimental results demonstrate that the improved gorilla search algorithm significantly enhances convergence speed and optimization accuracy, and exhibits good robustness. The SCAGTO algorithm demonstrates certain solution advantages in optimizing the pressure vessel design problem and welded beam design problem, verifying the superior optimization ability and engineering practicality of the SCAGTO algorithm.

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Bio-Inspired Swarm Intelligence Optimization Algorithm-Aided Hybrid TDOA/AOA-Based Localization

Cited by 18 publications

References 42 publications

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Bio-Inspired Internet of Things: Current Status, Benefits, Challenges, and Future Directions

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

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