Snake Optimizer: A novel meta-heuristic optimization algorithm

Hashim, Fatma A.; Hussien, Abdelazim G.

doi:10.1016/j.knosys.2022.108320

Cited by 596 publications

(232 citation statements)

References 67 publications

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“…The numerical results are presented in Table 7, which displays the design variables and minimum expenditures for each algorithm optimization, and Table 8 provides the statistics for 20 runs. In addition, Table 9 provides the algorithms from the literature (AOA [4], WCA [55], EPO [56], AHA [57], DMO [6], SO [58], HHO [8], SSA [11], COOT [14], HBA [52]. GJO [59]) obtained.…”

Section: Welded Beam Design Problemmentioning

confidence: 99%

“…The results show that LICRSA is one of the excellent methods to solve the problem. [56] 0.205411000 3.472341000 9.035215000 0.201153000 1.723589000 WCA [55] 0.205728000 3.470522000 9.036620000 0.205729000 1.726427000 AHA [57] 0.205730000 3.470492000 9.036624000 0.205730000 1.724853000 DMO [6] 0.205570500 3.256772400 9.036177000 0.205769600 1.696400000 SO [58] 0.205700000 3.470500000 9.036600000 0.205700000 1.724851931 HHO [8] 0.204039000 3.531061000 9.027463000 0.206147000 1.731990570 SSA [11] 0.205700000 3.471400000 9.036600000 0.205700000 1.724910000 COOT [14] 0.198827100 3.337971000 9.191986000 0.198834500 1.670301154 HBA [51] 0.205700000 3.470400000 9.036600000 0.205700000 1.724510000 GJO [59] 0.205620000 3.471900000 9.039200000 0.205720000 1.725220000 LICRSA 0.201941354 3.086318875 9.022514058 0.206392365 1.668814555…”

Section: Welded Beam Design Problemmentioning

confidence: 99%

“…Table 11 provides the statistics for 20 independent runs. In addition, Table 12 provides the results obtained by the algorithms (AOA [4], HHO [8], SMA [22], AO [23], SO [58], GJO [59], AVOA [60], AHA [57], COOT [14], SMO [5], CSA [61]) in the literature. According to the table, LICRSA has a minimum cost of 6014.62661, and the results also prove that LICRSA can handle the pressure vessel design problem very well.…”

Section: Pressure Vessel Design Problemmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Reptile Search Algorithm Based on Lévy Flight and Interactive Crossover Strategy to Engineering Application

et al. 2022

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In this paper, we propose a reptile search algorithm based on Lévy flight and interactive crossover strategy (LICRSA), and the improved algorithm is employed to improve the problems of poor convergence accuracy and slow iteration speed of the reptile search algorithm. First, the proposed algorithm increases the variety and flexibility of the people by introducing the Lévy flight strategy to prevent premature convergence and improve the robustness of the population. Secondly, an iteration-based interactive crossover strategy is proposed, inspired by the crossover operator and the difference operator. This strategy is applied to the reptile search algorithm (RSA), and the convergence accuracy of the algorithm is significantly improved. Finally, the improved algorithm is extensively tested using 2 test sets: 23 benchmark test functions and 10 CEC2020 functions, and 5 complex mechanical engineering optimization problems. The numerical results show that LICRSA outperforms RSA in 15 (65%) and 10 (100%) of the 2 test sets, respectively. In addition, LICRSA performs best in 10 (43%) and 4 (40%) among all algorithms. Meanwhile, the enhanced algorithm shows superiority and stability in handling engineering optimization.

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Section: Welded Beam Design Problemmentioning

confidence: 99%

Section: Welded Beam Design Problemmentioning

confidence: 99%

Section: Pressure Vessel Design Problemmentioning

confidence: 99%

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An Improved Reptile Search Algorithm Based on Lévy Flight and Interactive Crossover Strategy to Engineering Application

et al. 2022

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“…SO is a MH algorithm proposed by [24] in 2022 to mimic the mating behavior of snakes. Mating happens when the temperature is low and food is available.…”

Section: Snake Optimizer (So)mentioning

confidence: 99%

“…In the present work, a novel combined MH-based approach named Reptile Search Algorithm-Snake Optimizer (RSA-SO) is introduced to solve FS. The RSA-SO approach utilizes the best characteristics and capabilities of both the RSA [23] and SO [24] algorithms to obtain an optimal subset of informative features, where both are collaborated in a parallel mechanism. RSA and SO methods are among the most recent MH algorithms, and they show promising capabilities to solve FS problems with efficient balance between exploration and exploitation aspects.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Parallel Reptile Search Algorithm and Snake Optimizer Approach for Feature Selection

Al-Shourbaji

Kachare²,

Alshathri

et al. 2022

Mathematics

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Feature Selection (FS) is a major preprocessing stage which aims to improve Machine Learning (ML) models’ performance by choosing salient features, while reducing the computational cost. Several approaches are presented to select the most Optimal Features Subset (OFS) in a given dataset. In this paper, we introduce an FS-based approach named Reptile Search Algorithm–Snake Optimizer (RSA-SO) that employs both RSA and SO methods in a parallel mechanism to determine OFS. This mechanism decreases the chance of the two methods to stuck in local optima and it boosts the capability of both of them to balance exploration and explication. Numerous experiments are performed on ten datasets taken from the UCI repository and two real-world engineering problems to evaluate RSA-SO. The obtained results from the RSA-SO are also compared with seven popular Meta-Heuristic (MH) methods for FS to prove its superiority. The results show that the developed RSA-SO approach has a comparative performance to the tested MH methods and it can provide practical and accurate solutions for engineering optimization problems.

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Snake swarm optimization‐based deep reinforcement learning for resource allocation in edge computing environment

Kaliraj,

Sivakumar,

Premkumar

et al. 2024

Concurrency and Computation

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SummaryMobile edge computing has become popular in the past few years as a means of creating computing resources close to end‐user nodes at the network edge. Nodes—end users—demand work offloading to improve service utilization. Furthermore, when the number of users in mobile edge computing increases, the minimal resources deployed at the edge become a problem. Develop the idea of reinforcement learning using a metaheuristic technique intended to achieve effective resource allocation and resolve offloading issues to handle this issue. The ideal way to manage the implementation of mobile edge computing with a cognitive agent's help is to request compensation for all client necessities. To complete the infrastructure type for the Internet of Things (IoT), the operator information is combined with its distinctive methodology. Neural caching during task execution is provided by reinforcement learning based on snake swarm optimization (SSO). Neural caching during task execution is provided by reinforcement learning based on SSO. In the process of creating the cost mapping table and incentive factor‐based optimal resource allocation, this suggested method is applied to a contract with effective resource allocation among the end manipulators. Using performance metrics like delivery ratio, energy consumption, throughput, and delay, the suggested approach is put into practice and examined. It is also contrasted with traditional methods like Gray Wolf Optimization (GWO) ant colony optimization (ACO) and genetic algorithms (GA).

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Snake Optimizer: A novel meta-heuristic optimization algorithm

Cited by 596 publications

References 67 publications

An Improved Reptile Search Algorithm Based on Lévy Flight and Interactive Crossover Strategy to Engineering Application

An Improved Reptile Search Algorithm Based on Lévy Flight and Interactive Crossover Strategy to Engineering Application

An Efficient Parallel Reptile Search Algorithm and Snake Optimizer Approach for Feature Selection

Snake swarm optimization‐based deep reinforcement learning for resource allocation in edge computing environment

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