Hybrid Microgrid Many-Objective Sizing Optimization With Fuzzy Decision

Cao, Bin; Dong, Wen‐Kui; Lv, Zhihan; Gu, Yu; Singh, Surjit; Kumar, Pawan

doi:10.1109/tfuzz.2020.3026140

Cited by 216 publications

(75 citation statements)

References 35 publications

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“…2. We should improve consumers' satisfaction with products, improve their online and offline consumption experience, establish brand image, improve product competitiveness and attract consumers to become loyal users of cross-border products [43][44][45][46]. 3.…”

Section: Model Hypothesismentioning

confidence: 99%

Optimization algorithm and simulation of supply chain coordination based on cross-border E-commerce network platform

Wang

2021

J Wireless Com Network

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With the rapid development of cross-border e-commerce, the improvement of consumer satisfaction has become the focus of cross-border e-commerce platform optimization. Relying on advanced algorithm technology, it can realize the accurate and efficient matching between massive information and users, which is conducive to improving the user experience. Based on the consideration of consumer satisfaction, this paper constructs a dual channel supply chain composed of cross-border suppliers, cross-border e-commerce enterprises, retailers and consumers and studies the revenue and cost sharing contract of the supply chain under the conditions of centralized decision and decentralized decision. The research shows that cross-border e-commerce enterprises can choose to form revenue and expenditure sharing contracts through online and offline channels, optimize decentralized decision-making, and achieve win–win cooperation among supply chain entities.

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Section: Model Hypothesismentioning

confidence: 99%

Optimization algorithm and simulation of supply chain coordination based on cross-border E-commerce network platform

Wang

2021

J Wireless Com Network

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“…many-objective problems [11], [12], fuzzy-based optimization [13], and memetic techniques [14]. Suppose the conceivably complex problems at hand need the optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Arc Adaptive Grasshopper Optimization

et al. 2021

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The grasshopper optimization algorithm (GOA) has become one of the most widely used methods, regardless of its shortcomings in its performance and algorithm design. To further harmonize exploration and exploitation in the GOA, this paper proposed an enhanced variant of the primary method by incorporating chaos theory to address the problems of slow convergence, low solution accuracy, and stagnation. Structural code analysis and previous works reveal that the grasshopper algorithm's internal function is one of these shortcomings' primary sources. The proposed GOA-based method employed an adaptive arc function instead of the grasshopper algorithm's internal function to strengthen and balance the global exploration capabilities and local exploitation capacities without any expensive modifications. Additionally, the chaotic mapping strategy was adopted to update the individual positions of agents iteratively. The individuals near the current optimal solution were disturbed to regenerate the optimal solution and enhance the swarm's diversity. This idea improves the global inspection capacity and discourages falling into local optima. To verify the proposed technique's effectiveness, a comprehensive set of twenty-seven benchmark cases and three engineering design problems were used for validation. We compared the proposed GOA-based method with the WOA, SCA, GOA, DA, CSSA, CGSCA, CLPSO, GL25, and OBWOA. Additionally, we tested the designed GOA against LSHADE, SHADE, EBOwithCMAR, SaDE, MPEDE, and EPSDE. Simulation results demonstrated that the algorithm was substantially superior to the original technique. Its global optimization competence, search accuracy, and convergence performance were notably improved. The results expose the regulation of this internal factor, which significantly affects the quality of the results. Further information about this research and assistance with any request is available at http://aliasgharheidari.com. INDEX TERMSGrasshopper optimization algorithm, swarm intelligence, chaotic maps, engineering design problem.

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“…Artificial neural networks (ANN), as one of the most known AI-based solutions, have received increasing attraction recently [58][59][60][61][62]. More technically, deep learning-based [63][64][65][66], machine learning [67][68][69], decision making-based theories, feature selection-based solutions [70][71][72], extremer machine learning solutions [73][74][75][76], as well as hybrid searching algorithms that enhanced conventional multilayer perceptron like harris hawks optimization [77,78], whale optimizer [79,80], bacterial foraging optimization [81], chaos enhanced grey wolf optimization [82], moth-flame optimizer [74,83], many-objective sizing optimization [84][85][86][87][88][89], Driven Robust Optimization [90], ant colony optimization [91], and global numerical optimization [92]. These techniques are successfully employed in different aspects such as building design [93][94][95][96][97][98][99]…”

Section: Introductionmentioning

confidence: 99%

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Moayedi¹,

Mosavi²

2021

Preprint

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Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings' energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this work is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS-ANN). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA), are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model's optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90 % correlation) can adequately optimize the ANN. In this regard, this tool's prediction error declined by nearly 23, 18, and 36 % by applying the GOA, FA, and SFS techniques. Also, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

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Hybrid Microgrid Many-Objective Sizing Optimization With Fuzzy Decision

Cited by 216 publications

References 35 publications

Optimization algorithm and simulation of supply chain coordination based on cross-border E-commerce network platform

Optimization algorithm and simulation of supply chain coordination based on cross-border E-commerce network platform

Chaotic Arc Adaptive Grasshopper Optimization

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

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