A reliable approach for heat exchanger networks synthesis with stream splitting by coupling genetic algorithm with modified quasi-linear programming method

Feyli, Babak; Soltani, Hadi; Hajimohammadi, Reza; Fallahi-Samberan, Mehrab; Eyvazzadeh, Alireza

doi:10.1016/j.ces.2021.117140

Cited by 19 publications

(11 citation statements)

References 51 publications

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“…The algorithm adopts differentiated operations and strategies for data of different dimensions and adjusts corresponding parameters to realize multi-dimensional distributed collaboration, so as to complete the process of collaborative evolution. In the model, the neural network data are divided into five dimensions, and each dimension represents the subspace of the solution space [ 16 ]. In each iteration, the five dimensions evolve simultaneously.…”

Section: The Construction Of Behavior Recognition Model Based On Gene...mentioning

confidence: 99%

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network

Wang

Liu

2022

Computational Intelligence and Neuroscience

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With the extensive application of virtual technology and simulation algorithm, motion behavior recognition is widely used in various fields. The original neural network algorithm cannot solve the problem of data redundancy in behavior recognition, and the global search ability is weak. Based on the above reasons, this paper proposes an algorithm based on genetic algorithm and neural network to build a prediction model of behavior recognition. Firstly, genetic algorithm is used to cluster the redundant data, so that the data are in fragment order, and then it is used to reduce the data redundancy of different behaviors and weaken the influence of dimension on behavior recognition. Then, the genetic algorithm clusters the data to form subgenetic particles with different dimensions and carries out coevolution and optimal location sharing for subgenetic particles with different dimensions. Through simulation test, the algorithm constructed in this paper is better than genetic algorithm and neural network algorithm in terms of calculation accuracy and convergence speed. Finally, the prediction model is constructed by setting the initial value and threshold to predict the behavior recognition, and the results show that the accuracy of the model constructed in this paper is improved in the analysis of behavior recognition.

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Section: The Construction Of Behavior Recognition Model Based On Gene...mentioning

confidence: 99%

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network

Wang

Liu

2022

Computational Intelligence and Neuroscience

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“…The perturbation here is described as follows. Some heat loads Q n,best are selected randomly from the individual historical optimal HEN, which are then given perturbation as presented in Equation ( 37), finally the small heat units are also eliminated as Equation (35). The r 12 is a uniform random number distributed in the interval (0, 1), k is the perturbation factor.…”

Section: Back Substitution Of Optimumsmentioning

confidence: 99%

“…Recently, GA has been greatly developed in recent years. Feyli et al [35] combined GA with a technique named modified quasi-linear programming. Their method can get lower TAC of HENs due to the relatively linear behavior of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Cost-Optimal Heat Exchanger Networks Using a Novel Stochastic Algorithm and a Modified Stage-Wised Superstructure

et al. 2021

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Facing the current energy structure urgently needs to be transformed, heat exchanger network (HEN) can implement heat recovery and cost reduction by the arrangement for heat exchanges between cold and hot streams. The plenty of integer and continuous variables involved in HEN synthesis cause the results to be easily trapped in local optima. To avoid this situation, the mechanism of accepting imperfect solutions is added in a novel algorithm called Random Walk Algorithm with Compulsive Evolution. However, several potential solutions maybe abandoned by accepting imperfect solutions. To maintain the global searching ability, and at the same time, protecting the potential solutions during the optimization process, the limitations of accepting imperfect solutions are investigated in this work, then a back substitution strategy and elite optimization strategy based on algorithm are proposed. The former is to identify and adjust the inferior individuals in long-term stagnation while the latter is to keep and perform a fine search for the better solutions. Furthermore, a modified stage-wised superstructure is also developed to implement the flexible placement of utilities, which efficiently enlarges the solution domain. The validation of strategies and model is implemented by three cases, the results are lower, with 2219 $/year, 1280 $/year, and 2M $/year than the best published result, revealing the strong abilities of the proposed method in designing more economical HENs.

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“…The GA has a wide range of applications in a multitude of sectors, like in task scheduling problems in cloud computing [3], hybrid gene selection approach for cancer classification [4], heat exchanger networks [5], structural crack detection [6], nervous stomach nonlinear model [7], and mosquito dispersal model [8]. The significant potential of the heuristic computing scheme based on stochastic solvers is exploited to solve linear and nonlinear models by using the high predictive potential of artificial neural networks (ANNs) beneath the optimization of global and local search techniques [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Integrated Neuro-Evolution-Based Computing Paradigm to Study the COVID-19 Transposition and Severity in Romania and Pakistan

Shoaib

Abukhaled

Kainat

et al. 2022

Int J Comput Intell Syst

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Numerical treatment of the COVID-19 transposition and severity in Romania and Pakistan has been presented in this study, i.e., ANN-GA-SQP through artificial neural network genetic algorithms (ANN-GA) and sequential quadratic programming (SQP), a design of an integrated computational intelligent paradigm, COVID-19 is widely considered to be the greatest health threat humanity has ever faced. In terms of both health and economics, COVID-19 is a huge disaster. Many academics have looked at the COVID-19 model in their research papers, although they use different traditional techniques to represent it. The use of hybrid suggested solutions to solve this issue in the present article is significant, demonstrating the study's novelty. The SIR model of COVID-19 consists of a susceptible, infectious, and recovered class of population. The activation function for the construction of functions based on fitness in mean squared error sense is developed using nonlinear equations of the COVID-19 SIR model for the best performance of ANN-GA-SQP with the combined potential of GA and SQP of a network. While detailed refining is done with efficient local search with SQP, GAs operates as a global search. In addition, a neuron analysis will be presented to verify the effectiveness and complexity of the proposed method. Adam’s numerical methodology is applied to compare the sustainability and efficacy of the presented paradigm. Analytical evaluations of mean, median, and semi-interquartile range values, as well as Theil’s inequality coefficients, root mean squared error, and mean of absolute deviation) values have been observed. The convergence and correctness of the ANN-GA-SQP approach are further validated by statistical analyses.

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A reliable approach for heat exchanger networks synthesis with stream splitting by coupling genetic algorithm with modified quasi-linear programming method

Cited by 19 publications

References 51 publications

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network

Synthesis of Cost-Optimal Heat Exchanger Networks Using a Novel Stochastic Algorithm and a Modified Stage-Wised Superstructure

Integrated Neuro-Evolution-Based Computing Paradigm to Study the COVID-19 Transposition and Severity in Romania and Pakistan

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