Application of heuristic algorithms for design optimization of industrial heat pump

Oh, Bong Seong; Cho, Junhyun; Choi, Bongsu; Choi, Hong Wone; Kim, Min Soo; Lee, Gilbong

doi:10.1016/j.ijrefrig.2021.11.002

Cited by 11 publications

(6 citation statements)

References 30 publications

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“…(5) We assume that environmental factors (temperature, humidity) affect the cycle operation less. (6) We assume that the fitting formula of the isentropic efficiency of the cycle compressor is relatively accurate.…”

Section: Figurementioning

confidence: 99%

“…According to the process flow of the system, the heat released by Cond-1, Sub-C, and Cond-2 in the split double-flash thermodynamic cycle is defined as Q1, Q2, and Q3, which can be expressed by Formula (6).…”

Section: Figurementioning

confidence: 99%

“…This results in some thermal cycles with good heating performance, but complex structures not being favored in the industry. Through the thermodynamic model of the split flow double flash thermodynamic cycle, it can be concluded that the parameters of the closed-loop cycle need to be iteratively calculated through the model, and the control parameters within the cycle have a certain coupling effect with the component parameters [6] . If intelligent algorithms are applied to parameter optimization of split flow double flash thermal cycle, the key problem is establishing a suitable cycle parameter optimization path.…”

Section: Adaptation Analysis Of Intelligent Optimization Modelsmentioning

confidence: 99%

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Research on multi-objective parameter optimization of split flow dual flash thermodynamic cycle

Huang,

Zhang,

Wang

et al. 2024

J. Phys.: Conf. Ser.

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In addition to considering the intermediate pressure variables, the split flow double flash thermodynamic cycle also needs to consider the operating conditions and split flow coefficient of the cycle. Traditional algorithms require too much computation and have a longer response time. Faced with sudden operating conditions or changes in production conditions, it is difficult to quickly re-optimize the intermediate parameters of the split flow double flash thermal cycle and carry out corresponding system control. This article uses intelligent optimization algorithms instead of global search methods to optimize multi-objective parameters in a split flow double flash thermodynamic cycle. By summarizing and analyzing the characteristics of algorithms such as SIO, EA, and ANN, it is believed that the GWO algorithm in the SIO class is the most suitable for multi-objective parameter optimization of the split flow double flash thermal cycle. It is combined with the SA optimization algorithm to propose the SA-GWO algorithm with faster optimization speed and higher optimization accuracy, taking the initial position of the wolf pack as the optimization objective. Combined with the cycle thermodynamic model, a multi-objective parameter optimization model for the split flow double flash thermal cycle is established.

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Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Adaptation Analysis Of Intelligent Optimization Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Research on multi-objective parameter optimization of split flow dual flash thermodynamic cycle

Huang,

Zhang,

Wang

et al. 2024

J. Phys.: Conf. Ser.

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show abstract

“…e classical optimization algorithm tries to solve the load balancing of multicontrol architecture by uni ed scheduling in the control plane, which has achieved good results. In view of the actual situation of existing algorithms and switch migration process, heuristic algorithms, such as ant colony algorithm and genetic algorithm, have very good advantages in computing speed and resource consumption [12,13]. However, both ant colony algorithm and genetic algorithm have exposed many shortcomings in the course of many years of practice.…”

Section: Related Workmentioning

confidence: 99%

Load Balancing of SDN Controller for Migration Optimization Based on Metaheuristics

Min

2022

Mathematical Problems in Engineering

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With the existing power SDN network architecture, the improved switches migration algorithm is put forward. Through the advantages of the ant colony algorithm and genetic algorithm fusion, the migration process is optimized, and the fusion algorithm in switches uses the operation, such as restrictions in advance and pruning algorithm efficiency to eventually apply migration under multiple controllers to the switch. The efficiency and stability of switch migration under multicontroller are improved, and the performance of the RYU controller is compared with other algorithms. The results of the algorithm and performance comparison test show that the improved ant colony algorithm is reduced by 12.5% and 1.5%, which is a comparison of response delay; the accuracy of the migration operation of the algorithm can make full use of the resources of the controller and achieve the purpose of improving the migration efficiency and load balancing among multiple controllers.

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“…Most of them are swarm intelligence algorithms that imitate natural bodies, such as firefly algorithm, ant colony algorithm, and bee colony algorithm. They are applied to solve various optimization scheduling problems and are widely used in industry [ 6 ], network transmission [ 7 ], biology [ 8 ] and cloud computing [ 9 ]. Among them, the combination of neural networks and other algorithms has achieved considerable research success in image capture and retrieval [ 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Load Balancing Based on Firefly and Ant Colony Optimization Algorithms for Parallel Computing

Sun

et al. 2022

Biomimetics

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With the wide application of computational fluid dynamics in various fields and the continuous growth of the complexity of the problem and the scale of the computational grid, large-scale parallel computing came into being and became an indispensable means to solve this problem. In the numerical simulation of multi-block grids, the mapping strategy from grid block to processor is an important factor affecting the efficiency of load balancing and communication overhead. The multi-level graph partitioning algorithm is an important algorithm that introduces graph network dynamic programming to solve the load-balancing problem. This paper proposed a firefly-ant compound optimization (FaCO) algorithm for the weighted fusion of two optimization rules of the firefly and ant colony algorithm. For the graph, results after multi-level graph partitioning are transformed into a traveling salesman problem (TSP). This algorithm is used to optimize the load distribution of the solution, and finally, the rough graph segmentation is projected to obtain the most original segmentation optimization results. Although firefly algorithm (FA) and ant colony optimization (ACO), as swarm intelligence algorithms, are widely used to solve TSP problems, for the problems for which swarm intelligence algorithms easily fall into local optimization and low search accuracy, the improvement of the FaCO algorithm adjusts the weight of iterative location selection and updates the location. Experimental results on publicly available datasets such as the Oliver30 dataset and the eil51 dataset demonstrated the effectiveness of the FaCO algorithm. It is also significantly better than the commonly used firefly algorithm and other algorithms in terms of the search results and efficiency and achieves better results in optimizing the load-balancing problem of parallel computing.

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Application of heuristic algorithms for design optimization of industrial heat pump

Cited by 11 publications

References 30 publications

Research on multi-objective parameter optimization of split flow dual flash thermodynamic cycle

Research on multi-objective parameter optimization of split flow dual flash thermodynamic cycle

Load Balancing of SDN Controller for Migration Optimization Based on Metaheuristics

Load Balancing Based on Firefly and Ant Colony Optimization Algorithms for Parallel Computing

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