Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

Moayedi, Hossein; Mosavi, Amir

doi:10.3390/su13063198

Cited by 24 publications

(14 citation statements)

References 131 publications

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“…To verify the effectiveness of GCLPSO on multi-threshold image segmentation, GCLPSO will be compared with CLPSO, two improved algorithms SCADE and m_SCA, and three original algorithms SSA, SCA, and SMA, respectively. To ensure the validity and fairness of the experiments (Chen et al, 2021 ; Moayedi and Mosavi, 2021d ; Nosratabadi et al, 2021 ; Yang et al, 2021 ), all the algorithms involved in the comparisons were conducted under the same experimental conditions. Such a setting is one of the most crucial rules in the artificial intelligence community (Song et al, 2020 ; Thaher et al, 2020 ; Mousavi et al, 2021 ; Tavoosi et al, 2021 ).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

et al. 2021

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Maize is a major global food crop and as one of the most productive grain crops, it can be eaten; it is also a good feed for the development of animal husbandry and essential raw material for light industry, chemical industry, medicine, and health. Diseases are the main factor limiting the high and stable yield of maize. Scientific and practical identification is a vital link to reduce the damage of diseases and accurate segmentation of disease spots is one of the fundamental techniques for disease identification. However, one single method cannot achieve a good segmentation effect to meet the diversity and complexity of disease spots. In order to solve the shortcomings of noise interference and oversegmentation in the Otsu segmentation method, a non-local mean filtered two-dimensional histogram was used to remove the noise in disease images and a new elite strategy improved comprehensive particle swarm optimization (PSO) method was used to find the optimal segmentation threshold of the objective function in this study. The experimental results of segmenting three kinds of maize foliar disease images show that the segmentation effect of this method is better than other similar algorithms and it has better convergence and stability.

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Section: Experiments and Resultsmentioning

confidence: 99%

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

et al. 2021

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“…Heuristic pdf method Ant-lion optimization [42] coyote optimizer [43] Modified sine-cosine algorithm [44] Particle swarm optimization [45] From Tables 2-7, it is observed that the selection of DGs location is better found under GA for CPLF and CILF in comparison to other methods. By comparing the real power loss, sensitivity, and accuracy, it can be concluded that bus 17 is the best location for the placement of DG for both CPLF and CILF.…”

Section: Ga Methodsmentioning

confidence: 99%

“…Figures 6 and 7 provide the performance parameter comparison of GA with the heuristic method at different buses for CPLF and CILF. Moreover, in order to have comprehensive analysis and comparison for the DG location with GA and heuristic pdf, the results are compared with those obtained with the ant-lion optimization algorithm [42], coyote optimizer [43], modified sine-cosine algorithm [44], and particle swarm optimization [45]. The performance parameter evaluation for the estimation of DG location by using the ant-lion optimization algorithm [42], coyote optimizer [43], modified sine-cosine algorithm [44], and particle swarm optimization [45] is shown in Tables 4-7 for CPLF and CILF type of load.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…In this article, the heuristic probability distribution method and GA are applied for finding the best location and sizing of DG in the IEEE 33 bus system for the constant power load model (CPLF) and the constant impedance load model (CILF). The performance parameters show the superiority of GA over the heuristic probability distribution method and other existing methods such as ant-lion optimization algorithm [42], coyote optimizer [43], modified sine-cosine algorithm [44], and particle swarm optimization [45] in the IEEE 33 standard bus system. By considering lower real power losses, improved accuracy, and sensitivity, selection criteria of three DGs location are decided, and after that, size of DG is decided in terms of real and reactive power.…”

Section: Introductionmentioning

confidence: 96%

“…It is also observed that the proposed technique gives better results in comparison to existing techniques presented in literature [4,9,12,15,17,20,28,30,32,[35][36][37][38][39][40][41][42][43][44][45]. However, to analyze the efficacy of the proposed work in this paper, the results achieved are compared with the existing techniques such as ant-lion optimization algorithm [42], coyote optimizer [43], modified sine-cosine algorithm [44], and particle swarm optimization [45]. The GA seems to have the main advantage of utilizing entity definition, i.e., strings, instead of handling such entities themselves.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Location and Sizing of Distributed Generators in Power System Network with Power Quality Enhancement Using Fuzzy Logic Controlled D-STATCOM

et al. 2022

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This article presents the selection of location and sizing of multiple distributed generators (DGs) for boosting performance of the radial distribution system in the case of constant power load flow and constant impedance load flow. The consideration of placing and sizing of DGs is to meet the load demand. This article tries to overcome the limitations of existing techniques for determining the appropriate location and size of DGs. The selection of DG location is decided in terms of real power losses, accuracy, and sensitivity. The size of DG is measured in terms of real and reactive power. Both positioning and sizing of DG are analyzed with the genetic algorithm and the heuristic probability distribution method. The results are compared with other existing methods such as ant-lion optimization algorithm, coyote optimizer, modified sine-cosine algorithm, and particle swarm optimization. Further, the power quality improvement of the network is assessed by positioning D-STATCOM, and its location is decided on the basis of the nearby bus having poor voltage profile and high total harmonic distortion (THD). The switching and controlling of D-STATCOM are assessed with fuzzy logic controller (FLC) for improving the performance parameters such as voltage profile and THD at that particular bus. The proposed analytical approach for the system is tested on the IEEE 33 bus system. It is observed that the performance of the system with the genetic algorithm gives a better solution in comparison to heuristic PDF and other existing methods for determining the optimal location and size of DG. The introduction of D-STATCOM into the system with FLC shows better performance in terms of improved voltage profile and THD in comparison to existing techniques.

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Adaptive slime mould algorithm for optimal design of photovoltaic models

Lin

Ahmadianfar²,

Golilarz

et al. 2022

Energy Science & Engineering

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Solar energy is becoming more popular as it is a clean source of electricity. The design of photovoltaic (PV) cells has therefore captivated experts worldwide.The two key issues are the lack of an excellent model to define solar cells and the lack of data regarding PV cells. This scenario even impacts solar module performance (panels). The behavior of solar cells is described by the current versus voltage. Considering these values, the design challenge entails solving complicated nonlinear multimodal objectives. Different methods to figure out the parameters of the PV cells and panels have been suggested. They do not come up with the best solutions most of the time. Hence, a powerful and reliable optimizer is needed to derive the optimal parameters of these models.To this end, this study has developed an adaptive slime mould algorithm (ASMA) as a robust and precise optimization method. To implement the ASMA, four improvements are proposed: (1) a trigonometric-based mutation and a double-based best mutation are introduced to promote the global and local search; (2) a suitable mechanism to adaptively select the control parameters of the SMA; (3) a local escaping strategy; (4) an opposition-based learning operator to improve the best solution. The ASMA is employed to derive optimal parameters of PV models and assessed utilizing a total number of eight well-known optimization algorithms. The findings show that the ASMA is very competitive in terms of accuracy and convergence speed and that this is supported by a wealth of evidence. As a result, when assessing the parameters of the PV model, ASMA is a very efficient and robust optimizer.

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Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

Cited by 24 publications

References 131 publications

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

Optimal Location and Sizing of Distributed Generators in Power System Network with Power Quality Enhancement Using Fuzzy Logic Controlled D-STATCOM

Adaptive slime mould algorithm for optimal design of photovoltaic models

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