KGSA: A Gravitational Search Algorithm for Multimodal Optimization based on K-Means Niching Technique and a Novel Elitism Strategy

Golzari, Shahram; Zardehsavar, Mohammad Nourmohammadi; Mousavi, Amin; Saybani, Mahmoud Reza; Khalili, Abdullah; Shamshirband, Shahaboddin

doi:10.1515/math-2018-0132

Cited by 12 publications

(6 citation statements)

References 16 publications

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“…To resemble a real-life environment, we adopt a variation of the Foxhole Shekel function in our paper to represent the terrain, shown in Figure 1, which is formulated as expressed in Equation (1) [34],…”

Section: Operational Area Representationmentioning

confidence: 99%

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Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization

2021

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Collision-free distributed path planning for the swarm of unmanned aerial vehicles (UAVs) in a stochastic and dynamic environment is an emerging and challenging subject for research in the field of a communication system. Monitoring the methods and approaches for multi-UAVs with full area surveillance is needed in both military and civilian applications, in order to protect human beings and infrastructure, as well as their social security. To perform the path planning for multiple unmanned aerial vehicles, we propose a trajectory planner based on Particle Swarm Optimization (PSO) algorithm to derive a distributed full coverage optimal path planning, and a trajectory planner is developed using a dynamic fitness function. In this paper, to obtain dynamic fitness, we implemented the PSO algorithm independently in each UAV, by maximizing the fitness function and minimizing the cost function. Simulation results show that the proposed distributed path planning algorithm generates feasible optimal trajectories and update maps for the swarm of UAVs to surveil the entire area of interest.

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Section: Operational Area Representationmentioning

confidence: 99%

“…We adopted this terrain because there is a shortage of widely-accepted benchmarks in the field of trajectory planning for UAVs. Therefore, the local maxima of the landscape can be considered mountains [34].…”

Section: Operational Area Representationmentioning

confidence: 99%

Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization

2021

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“…To mimic a real-life terrain, we adopt a variant of the Foxhole Shekel function (Figure 3) in our paper to represent the landscape, which is formulated as expressed in [47] (1):…”

Section: B Terrain Representationmentioning

confidence: 99%

3D Optimal Surveillance Trajectory Planning for Multiple UAVs by Using Particle Swarm Optimization With Surveillance Area Priority

Ahmad

Msm

et al. 2020

IEEE Access

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The use of the unmanned aerial vehicle (UAV) has been regarded as a promising technique in both military and civilian applications. However, due to the lack of relevant laws and regulations, the misuse of illegal drones poses a serious threat to social security. In this paper, we develop a trajectory planner based on particle swarm optimization and a proposed surveillance area importance updating mechanism aimed at deriving three-dimensional (3D) optimal surveillance trajectories for multiple monitoring drones. We also propose a multi-objective fitness function in accordance with energy consumption, flight risk, and surveillance area priority in order to evaluate the trajectories generated by the proposed trajectory planner. Simulation results show that the trajectories generated by the proposed trajectory planner can preferentially visit important areas while obtaining a high fitness value in various practical situations.INDEX TERMS Particle swarm optimization, 3D path planning, surveillance area priority, multiple unmanned aerial vehicles.

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“…Data mining is a sub-field in artificial intelligence [1][2][3][4][5][6][7][8][9][10]. It has wide applications in classification and clustering of data in real world problems [11][12][13][14][15][16][17][18][19][20]. Nowadays, different classifiers have been gradually proposed through different underlying assumptions and mechanisms in order to enhance classification accuracy [21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Dealing with Imbalanced Dataset Leveraging Boundary Samples Discovered by Support Vector Data Description

Luo¹,

Parv飊²,

Garg³

et al. 2021

Computers, Materials &Amp; Continua

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These days, imbalanced datasets, denoted throughout the paper by ID, (a dataset that contains some (usually two) classes where one contains considerably smaller number of samples than the other(s)) emerge in many real world problems (like health care systems or disease diagnosis systems, anomaly detection, fraud detection, stream based malware detection systems, and so on) and these datasets cause some problems (like under-training of minority class(es) and over-training of majority class(es), bias towards majority class(es), and so on) in classification process and application. Therefore, these datasets take the focus of many researchers in any science and there are several solutions for dealing with this problem. The main aim of this study for dealing with IDs is to resample the borderline samples discovered by Support Vector Data Description (SVDD). There are naturally two kinds of resampling: Under-sampling (U-S) and oversampling (O-S). The O-S may cause the occurrence of over-fitting (the occurrence of over-fitting is its main drawback). The U-S can cause the occurrence of significant information loss (the occurrence of significant information loss is its main drawback). In this study, to avoid the drawbacks of the sampling techniques, we focus on the samples that may be misclassified. The data points that can be misclassified are considered to be the borderline data points which are on border(s) between the majority class(es) and minority class(es). First by SVDD, we find the borderline examples; then, the data resampling is applied over them. At the next step, the base classifier is trained on the newly created dataset. Finally, we compare the result of our method in terms of Area Under Curve (AUC) and F-measure and

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KGSA: A Gravitational Search Algorithm for Multimodal Optimization based on K-Means Niching Technique and a Novel Elitism Strategy

Cited by 12 publications

References 16 publications

Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization

Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization

3D Optimal Surveillance Trajectory Planning for Multiple UAVs by Using Particle Swarm Optimization With Surveillance Area Priority

Dealing with Imbalanced Dataset Leveraging Boundary Samples Discovered by Support Vector Data Description

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