Symmetric uncertainty based decomposition multi-objective immune algorithm for feature selection

Chai, Zhengyi; Li, Wangwang; Li, Yalun

doi:10.1016/j.swevo.2023.101286

Cited by 9 publications

(2 citation statements)

References 46 publications

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“…The experiment will refer to these three indicators to verify the quality of algorithmic solutions. Equations ( 20)- (22) show the principle of calculating the diversity of a population. The relevant code can be found on our project homepage: https://github.com/xiongfeide/AACO-LST (accessed on 29 September 2023).…”

Section: Principles Of the Experimentsmentioning

confidence: 99%

“…Although the TSP has been proven to be NP-hard theoretically, it still receives the attention of many researchers due to its applications in many practical scenarios, such as route planning, circuit board design, and military communication [12]. The ant colony algorithm, which has a strong advantage in solving TSP, is a heuristic optimization algorithm proposed by Dorigo [17], who was inspired by people's research results on the collective behavior of real ant colonies in nature, and it has the same advantages as genetic algorithm [11], particle swarm optimization [14][15][16][17][18][19][20][21], and immune algorithm [22][23][24][25], such as strong robustness and easy to combine with other methods, and has a wide range of application areas.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Ant Colony Optimization for Solving Large-Scale Traveling Salesman Problem

Tang,

Wei,

Jiang

et al. 2023

Mathematics

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The ant colony algorithm faces dimensional catastrophe problems when solving the large-scale traveling salesman problem, which leads to unsatisfactory solution quality and convergence speed. To solve this problem, an adaptive ant colony optimization for large-scale traveling salesman problem (AACO-LST) is proposed. First, AACO-LST improves the state transfer rule to make it adaptively adjust with the population evolution, thus accelerating its convergence speed; then, the 2-opt operator is used to locally optimize the part of better ant paths to further optimize the solution quality of the proposed algorithm. Finally, the constructed adaptive pheromone update rules can significantly improve the search efficiency and prevent the algorithm from falling into local optimal solutions or premature stagnation. The simulation based on 45 traveling salesman problem instances shows that AACO-LST improves the solution quality by 79% compared to the ant colony system (ACS), and in comparison with other algorithms, the PE of AACO-LST is not more than 1% and the Err is not more than 2%, which indicates that AACO-LST can find high-quality solutions with high stability. Finally, the convergence speed of the proposed algorithm was tested. The data shows that the average convergence speed of AACO-LST is more than twice that of the comparison algorithm. The relevant code can be found on our project homepage.

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Section: Principles Of the Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Ant Colony Optimization for Solving Large-Scale Traveling Salesman Problem

Tang,

Wei,

Jiang

et al. 2023

Mathematics

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Multi‐objective prairie dog optimization algorithm for IoT‐based intrusion detection

Sharma,

Kumar,

Dutta

2024

Internet Technology Letters

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Detecting unauthorized access, unusual activities, and data is significant for the security of IoT networks as it helps identify malfunctioning, faults, and intrusions. Intrusion detection methods analyze network information to identify potential misuse or intrusion attacks. This research proposes a multi‐objective prairie dog optimization algorithm (MPDA) to improve its ability to deal with feature selection problems. The proposed algorithm is modified by incorporating the concepts of an archive, grid, and non‐dominance. An archive and a grid are used to save intermediate best results and improve the diversity, respectively. The non‐dominance concept is employed to deal with multiple objectives. On the NSL‐KDD, CIC‐IDS2017, and IoTID20 datasets, MPDA achieves fewer features, higher accuracy, and lower false alarm rates. MPDA outperforms simple classifiers and state‐of‐art multiobjective optimization algorithms in intrusion detection.

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Feature selection using symmetric uncertainty and hybrid optimization for high-dimensional data

Sun

Ding

et al. 2023

Int. J. Mach. Learn. & Cyber.

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Symmetric uncertainty based decomposition multi-objective immune algorithm for feature selection

Cited by 9 publications

References 46 publications

An Adaptive Ant Colony Optimization for Solving Large-Scale Traveling Salesman Problem

An Adaptive Ant Colony Optimization for Solving Large-Scale Traveling Salesman Problem

Multi‐objective prairie dog optimization algorithm for IoT‐based intrusion detection

Feature selection using symmetric uncertainty and hybrid optimization for high-dimensional data

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