Grey wolf optimization for combined heat and power dispatch with cogeneration systems

Jayakumar, N.; Subramanian, S.; Ganesan, S.; Elanchezhian, E.B.

doi:10.1016/j.ijepes.2015.07.031

Cited by 183 publications

(72 citation statements)

References 29 publications

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“…This algorithm is inspired by the social hierarchy and hunting strategies of grey wolves in the wild. It can be regarded as a robust swarm-based optimizer [40][41][42][43][44][45]. The following discusses its working mechanism.…”

Section: Gwo Algorithmmentioning

confidence: 99%

SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

et al. 2018

Mathematical Problems in Engineering

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An improved Grey Wolf Optimization (GWO) algorithm with differential evolution (DEGWO) combined with fuzzy C-means for complex synthetic aperture radar (SAR) image segmentation was proposed for the disadvantages of traditional optimization and fuzzy C-means (FCM) in image segmentation precision. In the process of image segmentation based on FCM algorithm, the number of clusters and initial centers estimation is regarded as a search procedure that searches for an appropriate value in a greyscale interval. Hence, an improved differential evolution Grey Wolf Optimization (DE-GWO) algorithm is introduced to search for the optimal initial centers; then the image segmentation approach which bases its principle on FCM algorithm will get a better result. Experimental results in this work infers that both the precision and efficiency of the proposed method are superior to those of the state of the art.

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Section: Gwo Algorithmmentioning

confidence: 99%

SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

et al. 2018

Mathematical Problems in Engineering

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“…[9][10][11][12][13][14][15][16] The significant aim of the present work is to provide an efficient PEMFC model according to Amphlett equation model that simulates the practical performance of the cell at various operational circumstances. [31][32][33][34][35][36] So in this article, SBO is used to establish variables of PEMFC model. In the earlier decade, metaheuristic approaches developing as optimization issue have applied by many researchers.…”

Section: Introductionmentioning

confidence: 99%

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

2019

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Summary This study proposes precise modeling for the proton exchange membrane fuel cells which present desirable advantages compared with other energy management systems. The presented model can be applied for the simulation of the actual behavior of the proton exchange membrane fuel cells such as the electrical, electrochemical, and mechanical. In the present literature, a newly presented optimizer namely Satin Bowerbird is implemented for the evaluation of the proton exchange membrane fuel cell performance criteria. The Satin Bowerbird optimizer is an evolutionary algorithm that imitates the mating process of the Bowerbirds in the mating season. The Satin Bowerbird optimizer is applied to the different commercial benchmark of proton exchange membrane fuel cell stack to assess the performance of the proposed algorithm. The statistical study is also carried out to show the superiority of the proposed method compared with other schemes. The standard deviation for the Satin Bowerbird optimizer is obtained 0.0941 which is the lowest value amongst the other well‐known approaches. Also, the lowest sum of squared error is calculated for the proposed algorithm. Moreover, the validation of the presented method is done with the experimental data which shows good agreement between the experimental and modeling data.

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“…The CHPED optimization problem is a multimodal, nonconvex and constrained nonlinear programming problems . Considering coupling of power and heat, the valve point effects and transmission losses between multiple plants, the sophistication of the CHPED problem increases further …”

Section: Introductionmentioning

confidence: 99%

Social cognitive optimization with tent map for combined heat and power economic dispatch

Sun

2018

Int Trans Electr Energ Syst

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Summary Combined heat and power economic dispatch (CHPED) problem is a sophisticated constrained nonlinear optimization problem in a heat and power production system for assigning heat and power production to minimize the production costs. To address this challenging problem, a novel social cognitive optimization algorithm with tent map (TSCO) is presented for solving the CHPED problem. To handle the equality constraints in heat and power balance constraints, adaptive constraints relaxing rule is adopted in constraint processing. The novelty of our work lies in the introduction of a new powerful TSCO algorithm to solve the CHPED issue. The effectiveness and superiority of the presented algorithm is validated by conducting 2 typical CHPED cases. The numerical results show that the proposed approach has better convergence speed and solution quality than all other existing state‐of‐the‐art algorithms.

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Grey wolf optimization for combined heat and power dispatch with cogeneration systems

Cited by 183 publications

References 29 publications

SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

Social cognitive optimization with tent map for combined heat and power economic dispatch

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