Preaching-inspired swarm intelligence algorithm and its applications

Wei, Dong; Wang, Zhongbin; Si, Lina; Tan, Chao

doi:10.1016/j.knosys.2020.106552

Cited by 35 publications

(21 citation statements)

References 64 publications

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“…The proposed HPOA is compared with several well-known heuristic algorithms. Each of them contains different characteristics, including: Traditional POA algorithm [ 32 ]. The state-of-the-art WOA algorithm, in which is flexible and requires fewer parameters to be adjusted [ 42 ].…”

Section: Simulation and Discussion Of The Hpoa Algorithmmentioning

confidence: 99%

“…POA is a novel swarm intelligence algorithm proposed in 2021 [ 32 ]. The main inspiration of the algorithm is the process of religious spread: communication, competition, and development, which will be introduced at length in the following subsections.…”

Section: Poa Algorithmmentioning

confidence: 99%

“…The preaching optimization algorithm (POA) is a novel meta-heuristic algorithm proposed in 2021, which simulates the behavior of preachers in religious communication [ 32 ]. Preachers spread successors to improve the search range and select the next generation by the elite mechanism and weight consisted of fitness and location.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation

Zhu

Cao

et al. 2021

Entropy

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Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation.

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Section: Simulation and Discussion Of The Hpoa Algorithmmentioning

confidence: 99%

Section: Poa Algorithmmentioning

confidence: 99%

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A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation

Zhu

Cao

et al. 2021

Entropy

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“…al. ( 2021 ) 2021 2 17 Battle Royale Optimization Algorithm Mathematical and Engineering Optimization problems (Farshi 2021 ), Inverse Kinematics Problem (Farshi 2021 )Agahian,, Artificial Neural Network (Agahian and Akan 2021 ) Farshi ( 2021 ) 2021 14 18 Child Drawing Development Optimization Mathematical and Engineering Optimization problems (Abdulhameed and Rashid 2021 ) Abdulhameed and Rashid ( 2021 ) 2021 0 19 Cat and Mouse Based Optimizer Mathematical and Engineering Optimization problems (Dehghani et al 2021 ) Dehghani et. al.…”

Section: Survey On Recent Nature-inspired Optimization Algorithmsmentioning

confidence: 99%

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

2022

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Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.

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“…In the current study, working on FSW processes of Al5083 and Cu has been only addressed. The relationship between input and output parameters is quite evident in the subject of Design of Experiments (DoE) [16] and algorithms [17,18]. Algorithms provide no acceptable results in experiments with low data count and can be only implemented by at least 30 experimental data and above [19].…”

Section: Introductionmentioning

confidence: 99%

Prediction of mechanical properties and hardness of friction stir welding of Al 5083/pure Cu using ANN, ICA and PSO model

et al. 2022

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The effect of parameters involved in prediction of mechanical properties of Friction Stir Welding have been investigated in this study through modeling. For this purpose, the friction stir welding on both sheets of 5083 Aluminum (Al5083) and pure Copper (Cu) was experimentally conducted at first level. Three factors of Rotation Speed (RPM), Traverse Speed (mm/min) and the Tool’s pin angle was examined. During the tentative test, many experiments were not satisfactorily performed. Thanks to a more rigorous study hypothesis, proper samples were obtained by changing the geometry of different tools. In order to overcome the softness of aluminum sheets and the rise of shoulder diameter, and also to avoid the vertical instability of the sheet, shortening the pin’s length was suggested. In this paper, the Full Factorial method has been employed to evaluate the result of Artificial Neural Networks (ANN), Imperialist Competitive Algorithm, Particle Swarm Optimization, and also the effects of input parameters of the process on output parameters. Moreover, Al5083 and Cu joining sheets were analyzed. Micro-hardness and tensile tests have been based on the process’ input parameters to obtain mechanical properties. The function of ANN model demonstrates that it can estimate the number of mechanical properties with an adequate precision. Using the evaluation factors of mechanical properties and micro-hardness and also a R2 ~ 0.943 analysis, the optimum parameters of Al5083 and Cu joining sheets can be anticipated. The rotation speed of 1150 rpm, traverse speed of 40 mm/min, and pin angle of 2° are the optimum conditions based on the average review of analyses. These optimum conditions led to improve the maximum tensile force up to 15 ~ 21%.

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Preaching-inspired swarm intelligence algorithm and its applications

Cited by 35 publications

References 64 publications

A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation

A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Prediction of mechanical properties and hardness of friction stir welding of Al 5083/pure Cu using ANN, ICA and PSO model

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