Loose constraint genetic simplex algorithm based optimal design method for condenser

Abstract

The invention belongs to the field of nuclear power unit design, in particular to a loose constraint genetic simplex algorithm based optimal design method for a condenser. The method comprises the following steps: establishing a parameter evaluation model of the condenser; determining optimal variables, an objective function and a constraint function, and establishing an optimization model of the condenser; generating an initial population according to the optimization model; creating a loose restraint processing method for evaluating the individual quality; carrying out the genetic algorithm optimization of improved optimization strategy on the population, and generating a new population obtained after genetic evolution; generating the next generation of new population; utilizing the loose constraint processing method to evaluate the next generation of new population, and updating the restraint looseness; if meeting the termination condition, terminating the population evolution, and outputting the global optimal solution. The invention provides the loose restraint processing method for the complex nonlinear constraint problem of condenser optimization, and the loose restraint processing method is integrated into a hybrid optimization algorithm, so that the ability of optimization of the algorithm is greatly enhanced, and the effectiveness of condenser optimization design is ensured.

Description

technical field [0001] The invention belongs to the field of nuclear power plant design, and in particular relates to a condenser optimization design method based on a relaxed constraint genetic simplex algorithm. Background technique [0002] With the continuous development of new energy sources, nuclear energy, as a new energy source with high power density, clean and economical, has received more and more attention and has been widely used. Now, nuclear energy technology is generally leaping from the second generation to the third generation, the safety is getting higher and higher, and the power of a single stack is getting higher and higher. As the key heat exchange equipment in the nuclear power plant, the condenser has the largest weight and volume among all the equipment, and its performance also directly affects the thermal efficiency of the nuclear power plant. The increase of the power of a single stack leads to a decrease in the size and weight of the condenser. ...

AI Technical Summary

Problems solved by technology

Strong global search ability is its inherent characteristic attribute, but when dealing with complex constrained optimization problems, genetic algorithm and its improved algorithm still have the shortcomings of weak local search ability and low optimization efficiency, which often cannot meet the needs of solving problems

Simplex Algorithm (SM) is a classic local search optimization algorithm with strong local search ability, simple implementation and fast convergence speed, but poor global search ability

Due to the strong complementarity between the genetic algorithm and the simplex algorithm, and good combination performance, scholars Du Xiuli, Jiang Liping, etc. (2006) embedded the simplex algorithm into the genetic algorithm to form a hybrid optimization algorithm, and obtained a certain degree of performance improvement. A simple hybrid algorithm that only uses the traditional constraint processing method, the effect is often unsatisfactory when solving complex constraint optimization problems, and it is difficult to search for complex test functions (such as the g02 test function proposed by the scholar Runarsson). to the global optimal solution

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