Modified Flower Pollination Algorithm for Global Optimization

Abdel‐Basset, Mohamed; Mohamed, Reda; Saber, Safaa; Askar, Sameh; Abouhawwash, Mohamed

doi:10.3390/math9141661

Cited by 25 publications

(9 citation statements)

References 99 publications

(101 reference statements)

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“…The validation of the results found by the rIPA requires a comparison with the other meta-heuristics. For this purpose, the results of the rIPA were compared with the results of the different meta-heuristics including IPA [3], MFO [14], PSO [6,14], GSA [14,24], BA [13,14], FPA [1,14], SMS [7,14], FA [10,14] and GA [12,14]. In order to guarantee that all of these algorithms obtain their final solutions under the same conditions, population sizes were set to 30 and maximum evaluation number was taken equal to 30, 000 [14].…”

Section: Comparison Of Ripa With Other Meta-heuristics On Classical P...mentioning

confidence: 99%

“…Yang introduced Firefly algorithm (FA) [13] after analyzing flashing and signaling capabilities of the fireflies and Bat algorithm (BA) [10] by investigating bats and their echolocation properties. In addition to these algorithms, self and cross pollinating flowers became the source of inspiration for Yang and Flower Pollination algorithm (FPA) was proposed [1]. Mirjalili introduced or contributed to the developments of the meta-heuristics such as Moth-Flame Optimization (MFO) algorithm [14] based on the flying characteristics of the moths, Salp Swarm algorithm (SSA) [16] based on swarming behaviors of the salps and Harris Hawks Optimizer (HHO) [11] based on chasing styles of a special type of hawks.…”

Section: Introductionmentioning

confidence: 99%

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An improved immune plasma algorithm with a regional pandemic restriction

Aslan

Demirci

2022

SIViP

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The coronavirus and its global effect have increased the interests of researchers from different disciplines to the medical methods such as immune or convalescent plasma treatment. Immune Plasma algorithm (IPA) that is the first meta-heuristic referencing the steps of the immune plasma treatment as the name implies has been proposed recently and its potential has been investigated. In this study, a pandemic management strategy based on limiting the free movements between regions was modeled and integrated into the workflow of the IPA and a new variant called regional IPA (rIPA) was introduced. For analyzing the contribution of the proposed method, twelve numerical benchmark problems were solved. Also, the performance of the rIPA was investigated by solving a new big data optimization problem that requires minimization of the measurement noise of electroencephalography signals. The results obtained by the rIPA were compared with the fourteen well-known and state-of-art meta-heuristics. Comparative studies showed that managing the relationship between the individuals of the population as in the proposed regional model significantly contributes to the capabilities and rIPA outperforms other meta-heuristics for most of the test cases.

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Section: Comparison Of Ripa With Other Meta-heuristics On Classical P...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved immune plasma algorithm with a regional pandemic restriction

Aslan

Demirci

2022

SIViP

View full text Add to dashboard Cite

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“…first introduced elite reverse learning in the global search stage to enhance population diversity, second adopted an adaptive greedy strategy in the local search stage to increase the survey capability, and finally adopted dynamic transition probability and verify the improved FPA’s performance with the test functions. Mohamed 28 merged the FPA with the differential evolution algorithm to enhance its capability for global search. Wang and Meng 29 introduced Cauchy mutation in the FPA to prevent the algorithm from reaching a local optimum.…”

Section: Related Wordmentioning

confidence: 99%

K-means clustering algorithm based on improved flower pollination algorithm

et al. 2022

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.The K-means clustering algorithm is affected by the initial cluster center, resulting in a low accuracy of the clustering results. The standard flower pollination algorithm (FPA) has slow convergence and low optimization accuracy in the later stage. Therefore, the FPA is improved, and k-means is optimized accordingly. First, a random reverse learning strategy is used to uniformly distribute the population; second, the dynamic transition probability is used to balance the search mode to improve the overall performance of the algorithm; third, the nonlinear inertia weight parameter is introduced into the global search process to improve the global exploration ability; fourth, the optimal individual improves the diversity of the population while decreasing the probability of the algorithm failing. Six standard test functions are used to test the performance of improved flower pollination algorithm (IFPA), and the results show that IFPA is better than FPA in convergence speed and search optimization accuracy. The experimental comparative analysis of k-means cluster optimization based on improved flower pollination algorithm (IFPA-KM) on the University of California Irvine dataset shows that compared with k-means and FPA-KM, IFPA-KM improves the accuracy of clustering and has better stability.

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“…Heuristic algorithms have a wide range of applications. In dealing with optimization problems, many excellent optimization algorithms have been proposed in recent years, such as flower pollination algorithms [ 22 ], generalized normal distribution algorithms based on local search [ 23 ], and improved genetic algorithms [ 24 ]. Some excellent heuristic algorithms have been proposed to solve optimization problems, which have achieved good results in various fields [ 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm

Feng

2022

Sensors

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Bearings are widely used in various electrical and mechanical equipment. As their core components, failures often have serious consequences. At present, most parameter adjustment methods are still manual adjustments of parameters. This adjustment method is easily affected by prior knowledge, easily falls into the local optimal solution, cannot obtain the global optimal solution, and requires a lot of resources. Therefore, this paper proposes a new method for bearing fault diagnosis based on wavelet packet transform and convolutional neural network optimized by a simulated annealing algorithm. Firstly, the original bearing vibration signal is extracted by wavelet packet transform to obtain the spectrogram, and then the obtained spectrogram is sent to the convolutional neural network for parameter adjustment, and finally the simulated annealing algorithm is used to adjust the parameters. To verify the effectiveness of the method, the bearing database of Case Western Reserve University is used for testing, and the traditional intelligent bearing fault diagnosis methods are compared. The results show that the new method for bearing fault diagnosis proposed in this paper has a better and more reliable diagnosis effect than the existing machine learning and deep learning methods.

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Modified Flower Pollination Algorithm for Global Optimization

Cited by 25 publications

References 99 publications

An improved immune plasma algorithm with a regional pandemic restriction

An improved immune plasma algorithm with a regional pandemic restriction

K-means clustering algorithm based on improved flower pollination algorithm

A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm

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