Application of improved black hole algorithm in prolonging the lifetime of wireless sensor network

Zheng, Weimin; Liu, Ning; Chai, Qing-Wei; Liu, Yong

doi:10.1007/s40747-023-01041-3

Cited by 9 publications

(3 citation statements)

References 48 publications

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“…The heuristic algorithm has also been improved by adding many strategies. Zheng presents a Levy flight black edge regeneration black algorithm (LEBH) to speed up the convergence rate of BH [45]. Zheng applies the compact strategy to the snake optimization algorithm (SO).…”

Section: Equilibrium Optimizer Algorithmmentioning

confidence: 99%

A Game Model for Analyzing Wireless Sensor Networks of 5G Environment Based on Adaptive Equilibrium Optimizer Algorithm

Zheng,

Meng,

Liu

et al. 2023

Sensors

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Wireless sensors networks (WSNs) play an important role in life. With the development of 5G, its security issues have also raised concerns. Therefore, it is an important topic to study the offense and defense confrontation in WSNs. A complete information static game model is established to analyze the offense and defense confrontation problem of WSNs in 5G. An adaptive equilibrium optimizer algorithm (AEO) based on parameter adaptive strategy is proposed, which can jump out of the local optimal solution better. Experiments show that the optimization ability of AEO outperforms other algorithms on at least 80% of the 23 classical test functions of CEC. The convergence speed of AEO is better in the early stage of population iteration. The optimal offensive and defensive strategy under different offense and defense resources through simulation experiments is analyzed. The conclusion shows that when the offensive resources are large, the offender takes an indiscriminate attack. When the defense resources are small, the defender should defend the most important elements, and when the defense resources are large, the defender should allocate the same resources to defend each element to obtain the maximum benefit. This paper provides new solution ideas for the security problems under the offense and defense game in WSNs.

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Section: Equilibrium Optimizer Algorithmmentioning

confidence: 99%

A Game Model for Analyzing Wireless Sensor Networks of 5G Environment Based on Adaptive Equilibrium Optimizer Algorithm

Zheng,

Meng,

Liu

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Heuristic algorithms, which draw inspiration from natural laws, can be broadly classified into the following categories: physical methods based on principles such as gravity, temperature, and inertia, which randomly search for the optimal solution to optimization problems, for instance, methods like gravitational search [1]; simulated annealing [2]; and black hole algorithms [3]. Evolutionary algorithms, grounded in Darwin's theory, facilitate the gradual discovery of optimal solutions as the individuals within a population evolve through iterations during the search process.…”

Section: Introductionmentioning

confidence: 99%

Multi-Strategy Improved Particle Swarm Optimization Algorithm and Gazelle Optimization Algorithm and Application

Qin,

Zeng,

Sun

et al. 2024

Electronics

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In addressing the challenges associated with low convergence accuracy and unstable optimization results in the original gazelle optimization algorithm (GOA), this paper proposes a novel approach incorporating chaos mapping termed multi-strategy particle swarm optimization with gazelle optimization algorithm (MPSOGOA). In the population initialization stage, segmented mapping is integrated to generate a uniformly distributed high-quality population which enhances diversity, and global perturbation of the population is added to improve the convergence speed in the early iteration and the convergence accuracy in the late iteration. By combining particle swarm optimization (PSO) and GOA, the algorithm leverages individual experiences of gazelles, which improves convergence accuracy and stability. Tested on 35 benchmark functions, MPSOGOA demonstrates superior performance in convergence accuracy and stability through Friedman tests and Wilcoxon signed-rank tests, surpassing other metaheuristic algorithms. Applied to engineering optimization problems, including constrained implementations, MPSOGOA exhibits excellent optimization performance.

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“…Zheng et al proposed a new black hole algorithm by developing a compact strategy and an elitist learning strategy to improve the ability to jump out of the local optimum [ 13 ]. Zheng et al also proposed the Levy Flight Edge Regeneration Black Hole algorithm (LEBH) to speed up convergence and enhance optimization capabilities [ 14 ]. Furthermore, Pan et al conducted a comprehensively state-of-the-art investigation of the engineering applications employed by binary metaheuristic algorithms [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization

Zheng

Pang

Liu

et al. 2023

Sensors

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Indoor localization has broad application prospects, but accurately obtaining the location of test points (TPs) in narrow indoor spaces is a challenge. The weighted K-nearest neighbor algorithm (WKNN) is a powerful localization algorithm that can improve the localization accuracy of TPs. In recent years, with the rapid development of metaheuristic algorithms, it has shown efficiency in solving complex optimization problems. The main research purpose of this article is to study how to use metaheuristic algorithms to improve indoor positioning accuracy and verify the effectiveness of heuristic algorithms in indoor positioning. This paper presents a new algorithm called compact snake optimization (cSO). The novel algorithm introduces a compact strategy to the snake optimization (SO) algorithm, which ensures optimal performance in situations with limited computing and memory resources. The performance of cSO is evaluated on 28 test functions of CEC2013 and compared with several intelligent computing algorithms. The results demonstrate that cSO outperforms these algorithms. Furthermore , we combine the cSO algorithm with WKNN fingerprint positioning and RSSI positioning. The simulation experiments demonstrate that the cSO algorithm can effectively reduce positioning errors.

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Application of improved black hole algorithm in prolonging the lifetime of wireless sensor network

Cited by 9 publications

References 48 publications

A Game Model for Analyzing Wireless Sensor Networks of 5G Environment Based on Adaptive Equilibrium Optimizer Algorithm

A Game Model for Analyzing Wireless Sensor Networks of 5G Environment Based on Adaptive Equilibrium Optimizer Algorithm

Multi-Strategy Improved Particle Swarm Optimization Algorithm and Gazelle Optimization Algorithm and Application

A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization

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