A combined genetic algorithm/simulated annealing algorithm for large scale system energy integration

Yu, Hongmei; Fang, Haiqiu; Yao, Pingjing; Yao, Yuan

doi:10.1016/s0098-1354(00)00601-3

Cited by 110 publications

(51 citation statements)

References 4 publications

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“…It is a greedy algorithm, but its search process introduces random factors. When iterating to update the feasible solution, a solution worse than the current solution is accepted with a certain probability, so it is possible to get the optimal global solution by jumping out of the local optimal solution [12]. In [13][14][15], the simulated annealing algorithm was improved or combined with other algorithms to achieve a better performance in experiments.…”

Section: Simulated Annealingmentioning

confidence: 99%

Hybrid Approach with Improved Genetic Algorithm and Simulated Annealing for Thesis Sampling

et al. 2018

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Abstract:Sampling inspection uses the sample characteristics to estimate that of the population, and it is an important method to describe the population, which has the features of low cost, strong applicability and high scientificity. This paper aims at the sampling inspection of the master's degree thesis to ensure their quality, which is commonly estimated by random sampling. Since there are disadvantages in random sampling, a hybrid algorithm combined with an improved genetic algorithm and a simulated annealing algorithm is proposed in this paper. Furthermore, a novel mutation strategy is introduced according to the specialty of Shanghai's thesis sampling to improve the efficiency of sampling inspection; the acceleration of convergence of the algorithm can also take advantage of this. The new algorithm features the traditional genetic algorithm, and it can obtain the global optimum in the optimization process and provide the fairest sampling plan under the constraint of multiple sampling indexes. The experimental results on the master's thesis dataset of Shanghai show that the proposed algorithm well meets the requirements of the sampling inspection in Shanghai with a lower time-complexity.

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Section: Simulated Annealingmentioning

confidence: 99%

Hybrid Approach with Improved Genetic Algorithm and Simulated Annealing for Thesis Sampling

et al. 2018

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“…Xiao et al (2009) used evolutionary strategies and manipulations as the last stage of their method for improving water and heat exchanger network configurations. Liu et al (2013) used a hybrid algorithm called genetic algorithm combined with simulated annealing developed previously by Yu et al (2000) for solving the combined water and energy problem. They selected the mass flow rate and mass transfer temperatures of lean streams, the overall outlet concentrations, and the temperature difference contribution of streams as the decision variables.…”

Section: Solving Techniquesmentioning

confidence: 99%

A novel MILP approach for simultaneous optimization of water and energy: Application to a Canadian softwood Kraft pulping mill

Kermani

Périn-Levasseur

Benali

et al. 2017

Computers & Chemical Engineering

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Please cite this article as: Kermani, Maziar., Périn-Levasseur, Zoé., Benali, Marzouk., Savulescu, Luciana., & Maréchal, François., A novel MILP approach for simultaneous optimization of water and energy: Application to a Canadian softwood Kraft pulping mill.Computers and Chemical Engineering http://dx.doi.org/10. 1016/j.compchemeng.2016.11.043 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…Under this context, a hybrid algorithm named GA-SA was developed by Yu, Fang, Yao, and Yuan (2000), furthermore the orthogonal crossover (OCX) operator and effective crowding (EC) operator were introduced in their study to promote the performance of GA. In the GA-SA algorithm, SA accepts bad solutions according to the Metropolis rule, the acceptance probabilities of bad solutions follow the Boltzmann distribution at each temperature.…”

Section: Optimization Algorithm and Solution Approachmentioning

confidence: 99%

A systematic approach for synthesizing combined mass and heat exchange networks

Liu

El‐Halwagi

et al. 2013

Computers & Chemical Engineering

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A combined genetic algorithm/simulated annealing algorithm for large scale system energy integration

Cited by 110 publications

References 4 publications

Hybrid Approach with Improved Genetic Algorithm and Simulated Annealing for Thesis Sampling

Hybrid Approach with Improved Genetic Algorithm and Simulated Annealing for Thesis Sampling

A novel MILP approach for simultaneous optimization of water and energy: Application to a Canadian softwood Kraft pulping mill

A systematic approach for synthesizing combined mass and heat exchange networks

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