An Improved Pigeon-Inspired Optimisation Algorithm and Its Application in Parameter Inversion

Liu, Hanmin; Yan, Xuesong

doi:10.3390/sym11101291

Cited by 8 publications

(4 citation statements)

References 30 publications

(42 reference statements)

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“…PIO is a swarm intelligence algorithm inspired by pigeon's homing behavior. When a flock of pigeons flies a long way home, they use different navigation tools in different phases of their flight (Liu et al 2019). The pigeons continuously change their position according to the navigation tool (map and compass operator) following the best pigeon that has the best position.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

Disha

Waheed

2022

Cybersecurity

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To protect the network, resources, and sensitive data, the intrusion detection system (IDS) has become a fundamental component of organizations that prevents cybercriminal activities. Several approaches have been introduced and implemented to thwart malicious activities so far. Due to the effectiveness of machine learning (ML) methods, the proposed approach applied several ML models for the intrusion detection system. In order to evaluate the performance of models, UNSW-NB 15 and Network TON_IoT datasets were used for offline analysis. Both datasets are comparatively newer than the NSL-KDD dataset to represent modern-day attacks. However, the performance analysis was carried out by training and testing the Decision Tree (DT), Gradient Boosting Tree (GBT), Multilayer Perceptron (MLP), AdaBoost, Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) for the binary classification task. As the performance of IDS deteriorates with a high dimensional feature vector, an optimum set of features was selected through a Gini Impurity-based Weighted Random Forest (GIWRF) model as the embedded feature selection technique. This technique employed Gini impurity as the splitting criterion of trees and adjusted the weights for two different classes of the imbalanced data to make the learning algorithm understand the class distribution. Based upon the importance score, 20 features were selected from UNSW-NB 15 and 10 features from the Network TON_IoT dataset. The experimental result revealed that DT performed well with the feature selection technique than other trained models of this experiment. Moreover, the proposed GIWRF-DT outperformed other existing methods surveyed in the literature in terms of the F1 score.

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Section: Literature Reviewmentioning

confidence: 99%

Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

Disha

Waheed

2022

Cybersecurity

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“…Particle swarm optimization (PSO) was first invented by Dr.Eberhart and Dr. Kennedy [11]. It's a crowdbased heuristic algorithm used to model social comportment, like birds that cluster to suitable sites to find specific targets in multi-dimensional environments.…”

Section: A) Improved Particle Swam Optimizationmentioning

confidence: 99%

“…Particle is searched according to two factors to find the optimal solution: its earliest position (pbest) and the best position of all the other members. (gbest) [11][12][13].Shi et al called pbest the cognitive part and gbest the social part. Evacuation preparation is important in order to represent both geo and social actions.…”

Section: A) Improved Particle Swam Optimizationmentioning

confidence: 99%

Improved Particle Swam Optimization for Crowd Simulation Using Hybrid Agent Reinforcement Learning Algorithm

Pavithra¹,

Radhamani²,

Thangavel³

2021

ITII

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In an emergency route planning technique, simulating the dynamic crowd has route capacity constraints and global target of evacuating all crowd evacuees. To stimulate the crowd, the new arena is developed to know the real-time situation to face the crowd evacuation on exit point. The crowd evacuation is done with the process of Hybrid Agent Reinforcement Learning (HARL) algorithm consisting of Improved Multi-Agent Reinforcement Learning (IMARL) and State-Action-Reward-State-Action (SARSA). In the proposed work, the appropriate route selection mechanism focused on finding optimum evacuation route(s) is done in the first phase. Dynamic crowd can also be evacuated to find its way with the support of the HARL process in the second phase. The proposed HARL method can also be implemented with multi-objective improved particle swarm optimization (IPSO) technique for crowd simulation. The experimented results demonstrate the effectiveness of stability in the HARL process, which provides an improved performance for crowd simulation

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“…It is a population-based heuristic algorithm used for simulating social behavior, such as birds clustering to promising locations, in order to find accurate targets in multi-dimensional space. PSO uses groups of individuals (called particles) to perform searches as with evolutionary algorithms, and particles can be updated from each iteration to the other [26][27][28][29][30]. In order to find the optimal solution, each particle changes its search direction based on two factors: its best previous location (p best ) and all other members' best locations (g best ) [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data

Lee

et al. 2020

Symmetry

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In this paper, particle swarm optimization is incorporated into an improved bacterial foraging optimization algorithm, which is applied to classifying imbalanced data to solve the problem of how original bacterial foraging optimization easily falls into local optimization. In this study, the borderline synthetic minority oversampling technique (Borderline-SMOTE) and Tomek link are used to pre-process imbalanced data. Then, the proposed algorithm is used to classify the imbalanced data. In the proposed algorithm, firstly, the chemotaxis process is improved. The particle swarm optimization (PSO) algorithm is used to search first and then treat the result as bacteria, improving the global searching ability of bacterial foraging optimization (BFO). Secondly, the reproduction operation is improved and the selection standard of survival of the cost is improved. Finally, we improve elimination and dispersal operation, and the population evolution factor is introduced to prevent the population from stagnating and falling into a local optimum. In this paper, three data sets are used to test the performance of the proposed algorithm. The simulation results show that the classification accuracy of the proposed algorithm is better than the existing approaches.

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An Improved Pigeon-Inspired Optimisation Algorithm and Its Application in Parameter Inversion

Cited by 8 publications

References 30 publications

Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

Improved Particle Swam Optimization for Crowd Simulation Using Hybrid Agent Reinforcement Learning Algorithm

Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data

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