Enhanced Sparrow Search Algorithm With Mutation Strategy for Global Optimization

Ma, Bing; Lü, Peng; Zhang, Lufan; Liu, Yonggang; Zhou, Qiang; Chen, Yixin; Qi, Qisong; Hu, Yongtao

doi:10.1109/access.2021.3129255

Cited by 26 publications

(9 citation statements)

References 125 publications

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“…In this section, CGGWO will be compared with some state-of-the-art methods on IEEE CEC 2017 competition functions, which include LSHADE [62], EOBLSSA [63], ALCPSO [64], SHADE [65], CLSCA [66], SCADE [67] and MWOA [68]. SHADE is an improved version of JADE [69] which uses a different parameter adaptation mechanism based on this success-history-based adaptation.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Chaotic games driven grey wolf optimization: optimal performance design and comprehensive analysis

Tang,

Huang,

Chen

et al. 2024

Preprint

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Introduction : Meta-heuristics have attracted much attention due to their compatibility with other algorithms and excellent optimization ability. Gray wolf optimization (GWO) is also a meta-heuristic algorithm. GWO mainly tries to find the optimal solution by simulating the hierarchical structure and hunting behavior of gray wolves. GWO has the advantages of a relatively simple algorithm structure and fewer parameter Settings. Therefore, it is used in many fields, such as engineering and forecasting. Objectives GWO may have problems in harmonic convergence or be trapped into local optima for some complex tasks. An improved variant of basic GWO is proposed in this paper to efficiently alleviate this deficiency. Preferentially, chaos game optimization (CGO) is introduced into the conventional method to expand its neighborhood searching capabilities. Based on this strategy, we called the improved GWO as CGGWO. Methods To confirm the effectiveness and optimization ability of the CGGWO algorithm, CGGWO is compared with a set of meta-heuristics, including 7 basic meta-heuristics, 7 state-of-the-art meta-heuristics, and 5 enhanced GWO variants. The benchmark functions for comparison are IEEE CEC 2017. The dimensions(D) of the benchmark test function are 10, 30, 50, and 100. Moreover, CGGWO is applied to five practical engineering problems and two real-world benchmarks from IEEE CEC 2011. Non-parametric statistical Wilcoxon signed-rank and the Friedman tests are performed to monitor the performance of the proposed method. Results In benchmark function testing, CGGWO can find better solutions in most functions. In the Wilcoxon signed-rank and the Friedman tests, the P-value of CGGWO is mostly less than 5%. Among the five engineering problems, the feasible solution found by CGGWO is also the best compared with other methods. Conclusions In the benchmark function test, CGGWO has a better convergence effect than other methods and finds a better solution. From the results of the Wilcoxon signed-rank and the Friedman tests, we can see that the CGGWO results are statistically significant. In engineering problems, CGGWO can find feasible solutions.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Chaotic games driven grey wolf optimization: optimal performance design and comprehensive analysis

Tang,

Huang,

Chen

et al. 2024

Preprint

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“…As stated in Ref. 57 , the primary objective of optimizing the gear train design (GTD) is to achieve a gear transmission ratio as close as possible to 1/6.931. Let TA, TB, TD, and TF represent the number of teeth on gears A, B, D, and F, respectively.…”

Section: Egto For Engineering Design Problemsmentioning

confidence: 99%

Enhanced gorilla troops optimizer powered by marine predator algorithm: global optimization and engineering design

Hassan,

Kamel,

Mohamed

2024

Sci Rep

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This study presents an advanced metaheuristic approach termed the Enhanced Gorilla Troops Optimizer (EGTO), which builds upon the Marine Predators Algorithm (MPA) to enhance the search capabilities of the Gorilla Troops Optimizer (GTO). Like numerous other metaheuristic algorithms, the GTO encounters difficulties in preserving convergence accuracy and stability, notably when tackling intricate and adaptable optimization problems, especially when compared to more advanced optimization techniques. Addressing these challenges and aiming for improved performance, this paper proposes the EGTO, integrating high and low-velocity ratios inspired by the MPA. The EGTO technique effectively balances exploration and exploitation phases, achieving impressive results by utilizing fewer parameters and operations. Evaluation on a diverse array of benchmark functions, comprising 23 established functions and ten complex ones from the CEC2019 benchmark, highlights its performance. Comparative analysis against established optimization techniques reveals EGTO's superiority, consistently outperforming its counterparts such as tuna swarm optimization, grey wolf optimizer, gradient based optimizer, artificial rabbits optimization algorithm, pelican optimization algorithm, Runge Kutta optimization algorithm (RUN), and original GTO algorithms across various test functions. Furthermore, EGTO's efficacy extends to addressing seven challenging engineering design problems, encompassing three-bar truss design, compression spring design, pressure vessel design, cantilever beam design, welded beam design, speed reducer design, and gear train design. The results showcase EGTO's robust convergence rate, its adeptness in locating local/global optima, and its supremacy over alternative methodologies explored.

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“…Ma et al [ 67 ] improved the SSA searching performance by combining its components with elite opposition-based learning, Lévy flights, Tent chaos, and variable radius. The main purpose of the proposed method is to enhance the SSA population diversity, exploration, and exploitation capabilities and maintain the balance between them.…”

Section: Recent Variants Of Ssamentioning

confidence: 99%

Recent Versions and Applications of Sparrow Search Algorithm

Awadallah

Al-Betar

Doush

et al. 2023

Arch Computat Methods Eng

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This paper reviews the latest versions and applications of sparrow search algorithm (SSA). It is a recent swarm-based algorithm proposed in 2020 rapidly grew due to its simple and optimistic features. SSA is inspired by the sparrow living style of foraging and the anti-predation behavior of sparrows. Since its establishment, it has been utilized for a plethora of optimization problems in different research topics, such as mechanical engineering, electrical engineering, civil engineering, power systems, industrial engineering, image processing, networking, environment, robotics, planing and scheduling, and healthcare. Initially, the growth of SSA and its theoretical features are highlighted in terms of the number of published articles, citations, topics covered, etc. After that, the different extended versions of SSA are reviewed, where the main variations of SSA are produced to avoid premature convergence and to boost the diversity aspects. These extended versions are modifications and hybridization summarized with more focus on the motivations behind establishing these versions. Multi-objective SSA is also presented as another version to deal with Multi-objective optimization problems. The critical analysis of the main research gaps in the convergence behaviour of SSA is discussed. Finally, the conclusion and the possible future expansions are recommended based on the research works accomplished in the literature.

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Enhanced Sparrow Search Algorithm With Mutation Strategy for Global Optimization

Cited by 26 publications

References 125 publications

Chaotic games driven grey wolf optimization: optimal performance design and comprehensive analysis

Chaotic games driven grey wolf optimization: optimal performance design and comprehensive analysis

Enhanced gorilla troops optimizer powered by marine predator algorithm: global optimization and engineering design

Recent Versions and Applications of Sparrow Search Algorithm

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