Application of Elephant Herd Optimization Algorithm Based on Levy Flight Strategy in Intrusion Detection

Xu, Hui; Cao, Qi; Fang, Ce; Fu, Yingchun; Su, Jun; Wei, Siwei; Bykovyy, Pavlo

doi:10.1109/idaacs-sws.2018.8525848

Cited by 17 publications

(10 citation statements)

References 7 publications

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“…Oppositional-based learning EHO [108] Adaptive EHO [109] Hybrid EHO algorithms CBEHO, ATEHO, and BIEHO [110] EEHO-ElShaarawy [111] EEHO-Ismaeel [112] Fuzzy logic EHO [113] Hybrid EHO (EHO and GWO) [114] Hybrid EHO (GA and EHO) [115] Limit control parameter EHO [116] Extreme learning machine EHO [117] Global and local search EHO [118] Variants of EHO Binary EHO [119] Multi-objective EHO [120-121]…”

Section: K-means Eho [107]mentioning

confidence: 99%

Elephant Herding Optimization: Variants, Hybrids, and Applications

et al. 2020

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Elephant herding optimization (EHO) is a nature-inspired metaheuristic optimization algorithm based on the herding behavior of elephants. EHO uses a clan operator to update the distance of the elephants in each clan with respect to the position of a matriarch elephant. The superiority of the EHO method to several state-of-the-art metaheuristic algorithms has been demonstrated for many benchmark problems and in various application areas. A comprehensive review for the EHO-based algorithms and their applications are presented in this paper. Various aspects of the EHO variants for continuous optimization, combinatorial optimization, constrained optimization, and multi-objective optimization are reviewed. Future directions for research in the area of EHO are further discussed.

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Section: K-means Eho [107]mentioning

confidence: 99%

Elephant Herding Optimization: Variants, Hybrids, and Applications

et al. 2020

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“…MGEHO is compared with 9 other metaheuristics. These include EHO [19], enhanced EHO based on the c value (EEHO15) [22], OEHO [24], LFEHO [25], the IGWO [41], the EO [12], HHO [16], the WOA [15], and the SFO [14]. To make the experimental process fairer and more reliable, the parameters are set within each of the selected algorithms, as shown in Table 6.…”

Section: Comparison With Other Algorithmsmentioning

confidence: 99%

“…In their work, opposition-based learning was adopted to enhance the performance of EHO. Xu et al [25] proposed a novel algorithm, LFEHO, which combined Levy flight with EHO to overcome the problems of the original EHO Engineering with Computers algorithm falling into local optima and exhibiting poor convergence performance. Improved EHO algorithms such as IMEHO were presented by Li et al [26] The robustness and diversity of a given population are optimized by using a new global learning-based evolution strategy to update the velocities and positions of agents.…”

Section: Introductionmentioning

confidence: 99%

Manta ray foraging and Gaussian mutation-based elephant herding optimization for global optimization

Duan

Liu

et al. 2021

Engineering with Computers

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The elephant herding optimization (EHO) algorithm is a novel metaheuristic optimizer inspired by the clan renewal and separation behaviors of elephant populations. Although it has few parameters and is easy to implement, it suffers from a lack of exploitation, leading to slow convergence. This paper proposes an improved EHO algorithm called manta ray foraging and Gaussian mutation-based EHO for global optimization (MGEHO). The clan updating operator in the original EHO algorithm is replaced by the somersault foraging strategy of manta rays, which aims to optimally adjust patriarch positions. Additionally, a dynamic convergence factor is set to balance exploration and exploitation. The gaussian mutation is adopted to enhance the population diversity, enabling MGEHO to maintain a strong local search capability. To evaluate the performances of different algorithms, 33 classical benchmark functions are chosen to verify the superiority of MGEHO. Also, the enhanced paradigm is compared with other advanced metaheuristic algorithms on 32 benchmark functions from IEEE CEC2014 and CEC2017. Furthermore, a scalability test, convergence analysis, statistical analysis, diversity analysis, and running time analysis demonstrate the effectiveness of MGEHO from various aspects. The results illustrate that MGEHO is superior to other algorithms in terms of solution accuracy and stability. Finally, MGEHO is applied to solve three real engineering problems. The comparison results show that this method is a powerful auxiliary tool for handling complex problems.

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“…Meena et al (2018) used an improved EHO to solve a complex multi-objective distributed energy planning problem. Xu et al (2018) considered the shortcomings of the EHO that it is easy to fall into the local optimum in the search process, and proposed an improved EHO algorithm based on the Lévy flight strategy. The proposed algorithm overcomes the disadvantages of EHO which is easy premature and low convergence accuracy.…”

Section: Introductionmentioning

confidence: 99%

Improved elephant herding optimization using opposition-based learning and K-means clustering to solve numerical optimization problems

Wang

2021

J Ambient Intell Human Comput

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Elephant herding optimization (EHO) algorithm which is a new swarm intelligence optimization algorithm was proposed in 2015. Its cores are clan updating operator and separation operator. Although EHO has achieved great success, there are still some shortcomings. For example, EHO generates clans with a fixed number of individuals. The separation operator is only applied to the worst individual of each clan, easily leading to low diversity of the population. In order to improve the performance of EHO, in this paper, we improved EHO and proposed an opposition-based learning (OBL) elephant herding optimization (OBLEHO) algorithm. First, we introduced the OBL strategy into the process of initialization and clan leader updating. Second, we used K-Means clustering to classify individuals with similar positions into the same clan and improved the clan updating operator. Third, we increased the number of separated individuals and introduced the Cauchy mutation operator. Finally, we applied an elitist strategy to the proposed algorithm. Subsequently, we used 29 benchmark functions of CEC 2017 and 5 sets of traveling salesman problem (TSP) to verify the performance of OBLEHO. The experimental results indicated that OBLEHO has great competitiveness in terms of benchmark functions and TSP.

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Application of Elephant Herd Optimization Algorithm Based on Levy Flight Strategy in Intrusion Detection

Cited by 17 publications

References 7 publications

Elephant Herding Optimization: Variants, Hybrids, and Applications

Elephant Herding Optimization: Variants, Hybrids, and Applications

Manta ray foraging and Gaussian mutation-based elephant herding optimization for global optimization

Improved elephant herding optimization using opposition-based learning and K-means clustering to solve numerical optimization problems

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