A particle swarm optimizer with modified velocity update and adaptive diversity regulation

Çomak, Emre

doi:10.1111/exsy.12330

Cited by 9 publications

(8 citation statements)

References 33 publications

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“…The approach of adaptive velocity adjustment is introduced into the algorithm [32]. According to the evaluation of the actual situation, it can be seen that when the particle moves in the global optimal direction and the particle can consistently locate a better point, if the flight direction is no longer changed, the particle will fly to the optimal particle faster, thus rushing the convergence speed of the algorithm, as shown in (21):…”

Section: Adaptive Adjustment Of Velocitymentioning

confidence: 99%

“…The algorithm introduced adaptive velocity weighting and adaptive population splitting, which accelerated the convergence speed of the algorithm and helped the algorithm bounce out of the local optimal position. Çomak, E. et al [32] added the alpha parameter to the velocity update equation to control the influence of the global best and global worst particles on the velocity update equation, and the alpha value changed adaptively relative to the diversity measure. Lin, C.-J.…”

Section: Introductionmentioning

confidence: 99%

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Chaos Particle Swarm Optimization Enhancement Algorithm for UAV Safe Path Planning

Chu

Yang

2022

Applied Sciences

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For safe path planning of unmanned aerial vehicles (UAVs) in a three-dimensional (3D) environment with multiple threats, first, a cost function is introduced according to the terrain constraints and UAV overall performance constraints of the path planning problem. Then, improved nonlinear dynamic inertia weights (INDIW) are introduced into the particle swarm optimization (PSO) algorithm, and when the particles fall into the local optimum, the velocity is perturbed, and the velocity and improved nonlinear dynamic inertia weight PSO (VAINDIWPSO) algorithm are obtained. The algorithm improves the speed of convergence and fitness function value of the PSO algorithm. However, the impact of flyable path optimization is now not obvious. Therefore, to further enhance the overall performance of the VAINDIWPSO algorithm, the adaptive adjustment of the velocity is introduced, the chaotic initialization is carried out, and the improved logistic chaotic map is introduced into the algorithm, and an improved chaotic-VAINDIWPSO (IC-VAINDIWPSO) algorithm is obtained. Then, the corresponding relationship between the algorithm and constraints is used to efficiently search complicated environments and find paths with excessive security and small cost function. The simulation outcomes exhibit that in a complicated environment the IC-VAINDIWPSO algorithm substantially improves the speed of convergence of the algorithm, reduces the fitness function value of the algorithm and the initialization time of the algorithm, and the acquired path is additionally smoother. A near-optimal solution is obtained.

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Section: Adaptive Adjustment Of Velocitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chaos Particle Swarm Optimization Enhancement Algorithm for UAV Safe Path Planning

Chu

Yang

2022

Applied Sciences

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“…The PSO algorithm was developed by Kennedy and Eberhart (1995) later many variants (Çomak, 2019; Kılıç et al, 2021; Zomorodi‐moghadam et al, 2021) are developed for various problem types. The main purpose of the initial study was to examine the social behaviour of birds and fish swarms and to ensure their graphical simulation.…”

Section: System Model For Automated Test Designmentioning

confidence: 99%

Automated test design using swarm and evolutionary intelligence algorithms

et al. 2021

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The world's increasing dependence on computer‐assisted education systems has raised significant challenges about student assessment methods, such as automated test design. The exam questions should test the students' potential from various aspects, such as their intellectual and cognitive levels, which can be defined as attributes of the questions to assess student knowledge. Test design is challenging when various question attributes, such as category, learning outcomes, difficulty, and so forth, are considered with the exam constraints, such as exam difficulty and duration. In this paper, four contributions are provided to overcome test design challenges for the student assessment. First, a tool is developed to generate a synthetic question pool. Second, an objective function is designed based on the considered attributes. Third, the popular swarm and evolutionary optimization methods, namely particle swarm optimization, genetic algorithm, artificial bee colony, differential search algorithm are comparatively studied with novel methodologies applied to them. Finally, as the state of the art methods, artificial bee colony, and differential search algorithm are further modified to improve the solution of the test design. To perform the proposed algorithms, a dataset of 1000 questions is built with the proposed question attributes of the test design. Algorithms are evaluated in terms of their successes in both minimizing the objective function and running time. Additionally, Friedman's test and Wilcoxon rank‐sum statistical tests are applied to statistically compare the algorithms' performances. The results show that the improved artificial bee colony and the improved differential search provide better results than others in terms of optimization error and running time.

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“…The inertia weight parameter is important for optimization. Some of them are inertia weights that have been introduced in research [16][17][18][19][20][21]. In this study, the logarithm decreasing inertia weight (LogDIW) of PSO [22] was used to optimize the CNN hyperparameter.…”

Section: Introductionmentioning

confidence: 99%

Logarithm Decreasing Inertia Weight Particle Swarm Optimization Algorithms for Convolutional Neural Network

Murinto

Rosyda²

2022

Jurnal Informatika

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The convolutional neural network (CNN) is a technique that is often used in deep learning. Various models have been proposed and improved for learning on CNN. When learning with CNN, it is important to determine the optimal parameters. This paper proposes an optimization of CNN parameters using logarithm decreasing inertia weight (LogDIW). This paper is used two datasets, i.e., MNIST and CIFAR-10 dataset. The MNIST learning experiment, the CIFAR-10 dataset, compared its accuracy with the CNN standard based on the LeNet-5 architectural model. When using the MNIST dataset, CNN's baseline was 94.02% at the 5th epoch, compared to CNN's LogDIWPSO, which improves accuracy. When using the CIFAR-10 dataset, the CNN baseline was 28.07% at the 10th epoch, compared to the LogDIWPSO CNN accuracy of 69.3%, which increased the accuracy.

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A particle swarm optimizer with modified velocity update and adaptive diversity regulation

Cited by 9 publications

References 33 publications

Chaos Particle Swarm Optimization Enhancement Algorithm for UAV Safe Path Planning

Chaos Particle Swarm Optimization Enhancement Algorithm for UAV Safe Path Planning

Automated test design using swarm and evolutionary intelligence algorithms

Logarithm Decreasing Inertia Weight Particle Swarm Optimization Algorithms for Convolutional Neural Network

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