Study On Quantum Particle Swarm Optimization And Its Application In Optimization Of Thermal Network

In thermal engineering design,optimization is generally required,and optimization design has become the standard method in industrial design.For simple or small-scale optimization problems,the traditional optimization algorithm can solve,but for large-scale,multi-extreme optimization problems,the traditional optimization algorithm is no longer applicable,and the meta-heuristic algorithm is a more effective solution method.Quantum-behaved PSO(QPSO)is a new meta-heuristic algorithm based on the theory of particle swarm optimization(PSO),it has strong global search ability and good robustness.This paper first summarizes the mathematical model of thermal network optimization,and according to the characteristics of the model,select QPSO as the solution tool.Aiming at the shortage of the algorithm,the particle updating formula is improved,and a new algorithm for discrete optimization is designed.These algorithms are verified by programming with MATLAB and applied to the thermal network optimization.The paper has achieved the following results.(1)The properties of probability density function in QPSO are analyzed by mathematical method,and several probability density functions which meet the requirements of QPSO are constructed.Then,three kinds of particle position updating formulas are obtained by stochastic simulation,which are called exponential form,normal form and power form.The exponential form formula is the particle position updating formula used in the QPSO algorithm.The test results of the standard function show that the QPSO using the power form formula has better global search ability,so the power form QPSO(PQPSO),can be used as an improvement to the QPSO.The upper bounds and the recommended values of the parameters in the PQPSO are given by simulation experiments and numerical calculations.(2)Based on the idea of QPSO,a new algorithm for discrete space optimization is proposed,called discrete QPSO(DQPSO).The DQPSO has a similar structure to the genetic algorithm(GA),but does not need to set the crossover and mutation probability,so it has fewer control parameters.The algorithm is applied to the calculation of standard test function and knapsack problem.The experimental results show that the DQPSO is superior to other forms of binary PSO and GA in terms of computational accuracy and convergence.(3)The QPSO algorithm is used to solve the optimization of heavy oil steam injection pipe network.For the star-shaped and branched layout model of steam injection pipe network,a hybrid evolution method using QPSO is given and programmed with MATLAB software.The solution to the actual block layout of the branched pipe network shows that the total annual cost of using the QPSO is less than the original design result.After comparing the statistical results,the results obtained by the QPSO algorithm are better than the original PSO algorithm and GA.The comparison of two different forms of QPSO algorithm shows that the solution results are basically the same,but the PQPSO solves faster.(4)The DQPSO algorithm is applied to solve the diameter optimization of heating network.According to the characteristics of the problem,the DQPSO based on natural number coding is designed and implemented by MATLAB.Compared with the method commonly used in engineering,the annual conversion cost of DQPSO is significantly reduced.Compared with the results of several meta-heuristic algorithms,the DQPSO and tabu search(TS)algorithm are superior to other comparison algorithms in terms of computational stability and computation time.Compared with the TS algorithm,the DQPSO has lower computational cost and is not affected by the initial solution.