831 Words4 Pages

PATENT SEARCH ON OPTIMIZATION TECHNIQUES
Design and economic optimization of shell and tube heat exchangers using Artificial Bee Colony (ABC) algorithm.(Arzu Sencan Sahin, Bavram Kilic)
Shell and tube heat exchangers are probably the most common type of heat exchangers applicable for a wide range of operating temperatures and pressures. They are highly used in refrigeration, power generation, heating and air conditioning, chemical processes, manufacturing and medical applications.
A new shell and tube heat exchanger optimization design approach is developed. Artificial Bee Colony (ABC) has been applied to minimize the total cost of the equipment including capital investment and the sum of discounted annual energy expenditures related to pumping*…show more content…*

In one example, an operation to be performed by a circuit is selected. A plurality of hardware components for performing the operation are represented with a data flow graph having edges and nodes. A plurality of solutions for performing the operation are simulated as hardware component combinations represented as paths on the data flow graph. For each solution the cost including a number of edges and nodes traversed on the data flow graph and a supplemental sub-integer cost is determined and a solution is selected with the lowest cost as a hardware component combination for a circuit. Building energy consumption prediction method based on artificial bee colony algorithm and neural*…show more content…*

The method comprises the steps that firstly, the artificial bee colony algorithm is utilized for conducting weight value optimization on the neural network; secondly, the optimized neural network is utilized for predicting building energy consumption. The artificial bee colony algorithm is an optimizing algorithm simulating a bee colony and has the advantages that control parameters are fewer, implementation is easy, and calculation is convenient; compared with a particle swarm algorithm, a genetic algorithm and other intelligent computing methods, the artificial bee colony algorithm has the prominent advantages that in each iterative process, global search and local search are both performed, the probability of finding an optimal solution is greatly increased, local optimum is avoided to a great extent, and global convergence is enhanced. Thus, when the artificial bee colony algorithm is adopted to optimize the initial weight value of the neutral network, the accuracy of the neutral network predicting the building energy consumption is improved, and meanwhile the defects existing in weight value optimization of the neutral network at present can be overcome

In one example, an operation to be performed by a circuit is selected. A plurality of hardware components for performing the operation are represented with a data flow graph having edges and nodes. A plurality of solutions for performing the operation are simulated as hardware component combinations represented as paths on the data flow graph. For each solution the cost including a number of edges and nodes traversed on the data flow graph and a supplemental sub-integer cost is determined and a solution is selected with the lowest cost as a hardware component combination for a circuit. Building energy consumption prediction method based on artificial bee colony algorithm and neural

The method comprises the steps that firstly, the artificial bee colony algorithm is utilized for conducting weight value optimization on the neural network; secondly, the optimized neural network is utilized for predicting building energy consumption. The artificial bee colony algorithm is an optimizing algorithm simulating a bee colony and has the advantages that control parameters are fewer, implementation is easy, and calculation is convenient; compared with a particle swarm algorithm, a genetic algorithm and other intelligent computing methods, the artificial bee colony algorithm has the prominent advantages that in each iterative process, global search and local search are both performed, the probability of finding an optimal solution is greatly increased, local optimum is avoided to a great extent, and global convergence is enhanced. Thus, when the artificial bee colony algorithm is adopted to optimize the initial weight value of the neutral network, the accuracy of the neutral network predicting the building energy consumption is improved, and meanwhile the defects existing in weight value optimization of the neutral network at present can be overcome

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