Analysis of Network Attack and Defense Strategies Based on Pareto Optimum

Sun, Yang; Xiong, Wei; Yao, Zhonghua; Moniz, Krishna; Zahir, Ahmed

doi:10.3390/electronics7030036

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Genetic algorithm (GA) is motivated by genetic appliance [16]. Glowworm swarm technique is suggested and based on glowworms' performance [17]. Matched with old-style optimization techniques, the swarm procedures are more operative and used to solve complex system [18], [19].…”

Section: Glowworm Swam Techniquementioning

confidence: 99%

Cocktail parity problem solution based on modified blind extraction technique

Abdullah

Hamed

Al-Bakry

et al. 2022

IJEECS

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The Cocktail Party Problem solution is described as being the responsibility of isolating the voice signal in a noisy environment. Two popular methods for resolving this issue are blind source extraction and the Wiener filtering procedure. The blind source extraction approaches like fastICA, JADE and efficient fastICA are useful for extraction data from the mixed signals. The classical optimization techniques such as genetic algorithms or particle swarms for blind source extraction are mostly founded on the gradient and need the objective function, so the using of these techniques is very restricted. In the recent studies, the classical blind source separation techniques will not give the perfect solution for the cocktail parity problem. These methods struggle with convergence speed and accuracy issues as well. In order to enhance the separation process and get over these issues, this work adopted the glowworm swarm technique based on kurtosis as the objective function. The results show that the proposed technique produces good separation. The original and estimated signals are compared for similarity using the cross-correlation function.

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Section: Glowworm Swam Techniquementioning

confidence: 99%

Cocktail parity problem solution based on modified blind extraction technique

Abdullah

Hamed

Al-Bakry

et al. 2022

IJEECS

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“…In fact, swarm intelligence optimization algorithms have developed greatly and have been well applied in many fields [8]- [14] during recent years. For example, bee colony (BC) algorithm is motivated from bee colonies' nature behaviors.…”

Section: Introductionmentioning

confidence: 99%

Improved GSO Algorithms and Their Applications in Multi-Target Detection and Tracking Field

Hou

et al. 2020

IEEE Access

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Fast detection and high-precision tracking of multiple UAVs are the keys to achieving efficient low-altitude defense. However, as there are many hyper-parameters which need to be optimized in target detection network, commonly used methods such as random search method are computationally intensive and cannot quickly obtain multiple optimal hyper-parameters combinations. In addition, angular random walk due to low-frequency noise of speed sensor in servo loop can cause target tracking accuracy to decrease. Fortunately, those two problems can be regarded as a single-mode function optimization problem and a multi-mode function optimization problem, respectively. In the paper, in order to overcome the aforementioned problems, GSOM (glowworm swarm optimization mutation) algorithm and GSOMLDW (glowworm swarm optimization mutation linearly decreasing weight) algorithm are firstly proposed. Furthermore, the global convergence of GSOMLDW has been proven in the paper, which has not been analyzed in currently available literature. Then, experimental results on four multi-modal benchmark functions have strongly illustrated that the novel GSOM algorithm can enhance glowworms' memory ability and improve peak detection rate effectively. When it is used for optimizing hyper-parameters of multi-target detection network, it can be expected to obtain much more hyper-parameter combination selections. Meanwhile, experimental results on ten uni-modal benchmark functions have obviously demonstrated that GSOMLDW algorithm can balance glowworms' exploration and exploitation abilities powerfully and obtain superior global solution accuracy at last. When the GSOMLDW algorithm is used for servo system identification and drift error model identification, the final position error fluctuation after compensation is almost zero while it reaches 1500 urad before compensation. Consequently, the proposed method can effectively improve target tracking precision.INDEX TERMS GSO, system identification, multi-modal functions, multi-target tracking, anti-UAVs.

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