Visual Tracking with Levy Flight Grasshopper Optimization Algorithm

Zhang, Huanlong; Gao, Zeng; Zhang, Jie; Yang, Guanglu

doi:10.1007/978-3-030-31654-9_19

Cited by 11 publications

(3 citation statements)

References 20 publications

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“…The effectiveness of NBGOA was evaluated using 20 datasets with various sizes taken from the UCI datasets repository in comparison with five well-regarded optimization techniques in the feature selection field. Simulation results revealed that BGOA [78] NBGOA [79] BGOA [67] ECGOAs [80] LMGOA [81] ECGOA [82] CGOA [83] CGOA [84] SFECGOAs [85] OLCGOA [86] ECGOAs [87] ECAGOA [88] IGOA [70] EGOA [89] PGOA [90] LGOA [91] IGOA [92] AGOA [93] MI-LFGOA [94] LGOA [95] GOA_EPD [65] DJGOA [96] DQBGOA_MR [97] Fuzzy GOA [98] GO-FLC [99] EGOA-FC [100] AGOA [69] AGOA [101] GHO [102] self-adaptive GOA [103] OGOA [104] OBLGOA [105] IGOA [106] MOGOA [75] MOGOA [76] MOGOA [66] MOGOA [107] MOGOA [108] MOGOA [109] LWSGOA [110] MGOA [111] GOFS [112] PCA-GOA [113] OGOA [114] IGOA [115] Fractional-GOA…”

Section: ) Binary Grasshopper Optimization Algorithmmentioning

confidence: 99%

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

et al. 2021

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Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

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Section: ) Binary Grasshopper Optimization Algorithmmentioning

confidence: 99%

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

et al. 2021

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“…Results illustrated the good quality of the developed algorithms compared to GOA and other algorithms. An enhanced GOA scheme using a combination of levy flight with GOA (LGOA) is developed in [13]. The performance of LGOA was tested and compared with PSO and the basic GOA.…”

Section: Introductionmentioning

confidence: 99%

A new modified grasshopper optimization algorithm

Abdalla

2022

Bulletin EEI

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The grasshopper algorithm (GOA) is a recent algorithm. It is widely used in many applications and results in a good solution. The algorithm is simple and the accuracy in very high. The GOA has some limitations due to the use of linear comfort zone parameter that causes some difficulties in balancing between the exploration and exploitation which may lead to fall in a local optimum. In this paper a modification is made to improve the operation of GOA. A nonlinear function is developed to replace the linear comfort zone parameter. The benchmark of GOA authors is used for testing the performance improvement of the suggested modified GOA compared to the basic GOA. Results indicate that the MGOA outperforms original GOA, presenting a higher accuracy, faster convergence, and stronger stability. The proposed new modified GOA performs better than the original GOA.

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“…Nenavath et al [14] proposed a hybrid sinusoidal and cosine differential evolution algorithm, which improves the optimization performance through SCA global search and DE local search and is applied to target tracking. Zhang et al [15] used Levy flight to improve grasshopper algorithm. Levy flight can increase the diversity of population and enhance the ability to jump out of local optimum.…”

Section: Introductionmentioning

confidence: 99%

Dynamic spiral updating whale optimization algorithm for solving optimal power flow problem

Wang

Feng

Pan

et al. 2022

Preprint

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In order to solve the problems of the traditional whale optimization algorithm, such as slow convergence speed, low optimization precision and easy to fall into the local optimal solution, an improved algorithm combining elite disturbance opposition-based learning and dynamic spiral updating (OWOA) was proposed. Firstly, the whale population is initialized by opposition-based learning strategies to ensure the diversity of the population, and then elite whales are multiple chaos disturbed to avoid falling into local optimal solution; Secondly, the algorithm uses a dynamic spiral updating strategy, and dynamically adjusts the spiral shape with the iteration times, thus improving the optimization accuracy of the algorithm. Finally, using 12 classic reference functions, CEC2014 test set and CEC2017 test set to evaluate the effectiveness of OWOA. In addition, optimum power flow(OPF) is employed for estimating the efficacy of the OWOA in practical applications. The experimental results show that:compared with other algorithms, the algorithm in this paper has higher convergence speed and accuracy in unimodal function, multi-peak function and multi-dimensional function, and which is more competitive in providing optimal solutions for optimization problems.

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Visual Tracking with Levy Flight Grasshopper Optimization Algorithm

Cited by 11 publications

References 20 publications

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

A new modified grasshopper optimization algorithm

Dynamic spiral updating whale optimization algorithm for solving optimal power flow problem

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