CN105320988A - Parameter optimization method for wireless energy transmission system based on improved genetic algorithm - Google Patents

Parameter optimization method for wireless energy transmission system based on improved genetic algorithm Download PDF

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Publication number
CN105320988A
CN105320988A CN201410332449.8A CN201410332449A CN105320988A CN 105320988 A CN105320988 A CN 105320988A CN 201410332449 A CN201410332449 A CN 201410332449A CN 105320988 A CN105320988 A CN 105320988A
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wireless energy
energy transfer
transfer system
parameter optimization
population
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CN201410332449.8A
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禄盛
左晨阳
张艳
朴昌浩
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Chongqing University of Post and Telecommunications
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Chongqing University of Post and Telecommunications
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Abstract

The invention provides a parameter optimization method for a wireless energy transmission system based on an improved genetic algorithm. According to the invention, an improved crossover probability and mutation probability calculation method and an improved individual selection strategy are introduced, a fitness value variance of the current population is calculated, the crossover probability and the mutation probability of the current evolution are calculated according to the fitness value variance, and the individual selection strategy selects individuals according to the distance between individuals and determines whether the selected individuals get into the next generation or not. The improved crossover probability and mutation probability calculation method can accelerate the speed of algorithm convergence and the global searching ability. The improved individual selection strategy can ensure the population diversity and enhance the global searching ability of the algorithm. The parameter optimization method provided by the invention can be widely applied to parameter optimization problems of the wireless energy transmission system.

Description

A kind of wireless energy transfer system parameter optimization method based on Improving Genetic Algorithm
Technical field
The invention belongs to wireless power transmission technology and novel energy switch technology field, be specifically related to a kind of wireless energy transfer system parameter optimization method based on Improving Genetic Algorithm.
Background technology
Wireless energy transmission technology carries out Energy Transfer by the electromagnetic field in air, do not need electrical and physical connection.Traditional wired delivery of electrical energy mode can not meet the needs of some specific occasions, and the problem such as conductor that electric wire exists friction, wearing and tearing and exposes, and is easy to produce electric spark, has influence on safety and the stability of personal safety and consumer.Wireless energy transfer can solve the problems existed in wire transmission, this application prospect that wireless energy transmission technology has been had, and wireless energy transmission technology has become the hot-point and frontier of research.
At present to the research of wireless energy transmission technology be all with transfer efficiency and through-put power for performance index, go out transfer efficiency and the relation between through-put power and a certain parameter by the derivation of equation, then use Mathematical method to obtain optimum parameter value.This method is not considered from system level, be only in attention location system in a certain respect, the parameter value solving out may make transfer efficiency and power be in locally optimal solution, also may be inferior solution.And along with system topology is complicated, coil number increases, system equation there will be high-order nonlinear, use method in the past to carry out parameter optimization, and the optimized parameter of trying to achieve may be unreachable in practice.Genetic algorithm is a kind of optimized algorithm of simulation biological evolution, it is applied in the parameter optimization of wireless energy transfer system, can solves very easily and obtain system optimal parameter value.But need during individual choice according to adaptive value in traditional genetic algorithm, process constraint condition uses penalty function method usually, and the setting parameter of penalty function becomes very difficult.And along with the increase of evolutionary generation, the pattern that the crossover probability remained unchanged and mutation probability can destroy, speed of convergence is slow.
Summary of the invention
For Problems existing in above-mentioned background technology, the present invention proposes a kind of wireless energy transfer system parameter optimization method based on Improving Genetic Algorithm, the method is passed through the crossover probability in traditional genetic algorithm and mutation probability, and individual choice strategy improves, genetic algorithm after improvement is applicable to solving multiparameter in wireless energy transfer system, high dimension, the optimization problem of multiple constraint.
Realizing the technical solution adopted in the present invention is: a kind of wireless energy transfer system parameter optimization method based on Improving Genetic Algorithm, specifically comprises the following steps:
Step 1: the parameter optimized needed for wireless energy transfer system, transmission performance indicators, the optimized variable of setting improved adaptive GA-IAGA and objective function, and the span and the constraint condition that set optimized variable according to system actual operating conditions;
Step 2: Population Size, stopping criterion for iteration in setting improved adaptive GA-IAGA, and initialization population;
Step 3: the variance calculating current population at individual fitness value and Population adaptation angle value, and calculate crossover probability and mutation probability according to the variance of suitable Population adaptation angle value;
Step 4: carry out selection according to the distance between individuality individual;
Step 5: heuristic intersection and non-uniform mutation are carried out to individuality according to crossover probability and mutation probability, and elite's retention strategy is used to the filial generation after generating;
Step 6: judge whether to reach algorithm iteration end condition, if reached, exports the parametric results of optimization, otherwise returns step 3.
Described wireless energy transfer system comprises magnet coupled resonant type wireless energy transmission system and induction type wireless energy transfer system;
In described step 3, crossover probability P cwith mutation probability P mcalculate according to following formula:
P c = 1 1 + e 1 varianceFitness + 0.4 , P m = 0.3 1 + e - 1 varianceFitness , Wherein varianceFitness is the variance of Population adaptation angle value.
Described step 4 is specifically:
Step 41: Stochastic choice two individual i and j, the distance calculated between individuality is d i, j;
Step 42: judge d i, jwhether be less than k, k is a constant of setting, if it is enter the next generation according to the individuality that fitness value between feasible solution is good, when infeasible solution compares with feasible solution, feasible solution enters the next generation, exceed when comparing between infeasible solution degree of restraint little enter the next generation;
Beneficial effect of the present invention is: the present invention can solve wireless energy transfer system Parametric optimization problem, by introducing the Improving Genetic Algorithm of new selection strategy, crossover and mutation probability, improve algorithm the convergence speed, strengthen local search ability, and without the need to too much, algorithm parameter is set, at the wireless energy transfer system design initial stage, there is good directive significance, cost-saving.
Accompanying drawing explanation
Fig. 1 is a kind of wireless energy transfer system parameter optimization method process flow diagram based on Improving Genetic Algorithm
Embodiment
The present invention proposes a kind of simple, efficient wireless energy transfer system parameter optimization method.Below in conjunction with accompanying drawing, parameter optimization method of the present invention is described in detail.
Step 1: determine the required parameter optimized of wireless energy transfer system, such as coil turn, coil radius, frequency of operation, transmission range, load voltage etc. are as the optimized variable of genetic algorithm.Determine the transmission performance indicators of wireless energy transfer system, as with transmission range and most effective for performance index, the optimization object function setting genetic algorithm according to performance index is transmission range and transfer efficiency is maximum.Then according to the condition of system works, the type selecting scope as the electric current in loop, electric capacity both end voltage, device defines one group of constraint condition relevant to optimized variable, and the span of optimized variable.
Step 2: Population Size, stopping criterion for iteration in setting improved adaptive GA-IAGA, and initialization population.Stopping criterion for iteration can be maximum evolution number of times, or the number of times that optimum individual remains unchanged.
Step 3: the variance calculating current population at individual fitness value and Population adaptation angle value, and calculate crossover probability and mutation probability according to the variance of suitable Population adaptation angle value.
Crossover probability P cwith mutation probability P mcalculate according to following formula:
P c = 1 1 + e 1 varianceFitness + 0.4 , P m = 0.3 1 + e - 1 varianceFitness , Wherein varianceFitness is the variance of Population adaptation angle value.Along with evolution increases, population at individual becomes more and more concentrated, if crossover probability and mutation probability immobilize, then algorithm the convergence speed is slow, is not easy to obtain globally optimal solution.So change crossover probability and mutation probability according to the variance of Population adaptation angle value, large at evolution initial stage crossover probability, mutation probability is little can be conducive to global search, later stage of evolution crossover probability is little, mutation probability is conducive to Local Search greatly.
Step 4: carry out selection according to the distance between individuality individual.
First, Stochastic choice two individual i and j from population, calculate the distance d between individuality i, j.
Then, the spacing d of individuality is judged i, jwhether be less than k, k is a constant of setting, if meet, the individuality good according to fitness value between feasible solution enters the next generation, and when infeasible solution compares with feasible solution, feasible solution enters the next generation, exceed when comparing between infeasible solution degree of restraint little enter the next generation.Be all similar individuals due to what compare at every turn, do not ignore infeasible solution, the diversity of population at individual can be kept like this, prevent algorithm Premature Convergence in locally optimal solution.
Step 5: heuristic intersection and non-uniform mutation are carried out to individuality according to crossover probability and mutation probability, and elite's retention strategy is used to the filial generation after generating.
Suppose that the fitness of individual x is greater than y, then heuristic intersection is according to carrying out as follows:
x ′ = x + r ( x - y ) y ′ = x , Wherein r is the random number between (0,1), if x ' and y ' is not in solution space, then regenerates random number and intersects.Elite's retention strategy being used to the filial generation generated after cross and variation, substituting the poorest individuality with having best fitness individuality in current filial generation.
Step 6: judge whether to reach algorithm iteration end condition, if reached, exports the parametric results of optimization, otherwise returns step 3.
Above embodiment only in order to thought of the present invention is described, can not limit protection scope of the present invention with this, and every technological thought proposed according to the present invention, any change that technical scheme is made, all falls into protection scope of the present invention.

Claims (4)

1., based on a wireless energy transfer system parameter optimization method for Improving Genetic Algorithm, it is characterized in that comprising the steps:
Step 1: the parameter optimized needed for wireless energy transfer system, transmission performance indicators, the optimized variable of setting improved adaptive GA-IAGA and objective function, and the span and the constraint condition that set optimized variable according to system actual operating conditions;
Step 2: Population Size, stopping criterion for iteration in setting improved adaptive GA-IAGA, and initialization population;
Step 3: the variance calculating current population at individual fitness value and Population adaptation angle value, and calculate crossover probability and mutation probability according to the variance of suitable Population adaptation angle value;
Step 4: carry out selection according to the distance between individuality individual;
Step 5: heuristic intersection and non-uniform mutation are carried out to individuality according to crossover probability and mutation probability, and elite's retention strategy is used to the filial generation after generating;
Step 6: judge whether to reach algorithm iteration end condition, if reached, exports the parametric results of optimization, otherwise returns step 3.
2. a kind of wireless energy transfer system parameter optimization method based on Improving Genetic Algorithm as claimed in claim 1, is characterized in that: described wireless energy transfer system comprises magnet coupled resonant type wireless energy transmission system and induction type wireless energy transfer system.
3. a kind of wireless energy transfer system parameter optimization method based on Improving Genetic Algorithm as claimed in claim 1, is characterized in that: in described step 3, crossover probability P cwith mutation probability P mcalculate according to following formula:
wherein varianceFitness is the variance of Population adaptation angle value.
4. a kind of wireless energy transfer system parameter optimization method based on Improving Genetic Algorithm as claimed in claim 1, is characterized in that: described step 4 specifically:
Step 41: Stochastic choice two individual i and j, the distance calculated between individuality is d i, j;
Step 42: judge d i, jwhether be less than k, k is a constant of setting, if it is enter the next generation according to the individuality that fitness value between feasible solution is good, when infeasible solution compares with feasible solution, feasible solution enters the next generation, exceed when comparing between infeasible solution degree of restraint little enter the next generation.
CN201410332449.8A 2014-07-14 2014-07-14 Parameter optimization method for wireless energy transmission system based on improved genetic algorithm Pending CN105320988A (en)

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Cited By (8)

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CN108574345A (en) * 2017-03-10 2018-09-25 重庆邮电大学 A kind of wireless power transmission equipment transmitting terminal self-adapting tuning device and tuning methods
CN109687603A (en) * 2019-02-18 2019-04-26 兰州交通大学 Consider the ICPT system resonance compensating parameter optimization method of signal and electric energy parallel transmission
CN110782080A (en) * 2019-10-21 2020-02-11 浙江大学 Electric power system structure optimization method based on population performance sorting selection
CN111178528A (en) * 2019-12-20 2020-05-19 江苏方天电力技术有限公司 Elite genetic algorithm improvement method applied to wireless charging system
CN111709559A (en) * 2020-05-29 2020-09-25 杭州电子科技大学 Intelligent transportation scheduling optimization method based on improved genetic algorithm
CN113301576A (en) * 2021-05-26 2021-08-24 南京邮电大学 Cellular network resource allocation method based on improved genetic algorithm
CN113794286A (en) * 2021-09-06 2021-12-14 湖北工业大学 Parameter optimization method and device for wireless power transmission system
CN113806884A (en) * 2021-09-02 2021-12-17 广东泰坦智能动力有限公司 Resonant converter design parameter selection method based on genetic algorithm

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CN1900956A (en) * 2006-07-11 2007-01-24 南京大学 Design method for improved mixed genetic algorithm optimizing water quality model parameter
CN102446236A (en) * 2010-10-13 2012-05-09 中国石油大学(华东) Automatically optimized piping arrangement method based on improved genetic algorithm
CN102523585A (en) * 2011-11-25 2012-06-27 北京交通大学 Cognitive radio method based on improved genetic algorithm

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US20050074090A1 (en) * 2003-10-07 2005-04-07 Bruker Axs Gmbh Method of determining parameters of a sample by X-ray scattering applying an extended genetic algorithm including a movement operator
CN1900956A (en) * 2006-07-11 2007-01-24 南京大学 Design method for improved mixed genetic algorithm optimizing water quality model parameter
CN102446236A (en) * 2010-10-13 2012-05-09 中国石油大学(华东) Automatically optimized piping arrangement method based on improved genetic algorithm
CN102523585A (en) * 2011-11-25 2012-06-27 北京交通大学 Cognitive radio method based on improved genetic algorithm

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108574345A (en) * 2017-03-10 2018-09-25 重庆邮电大学 A kind of wireless power transmission equipment transmitting terminal self-adapting tuning device and tuning methods
CN109687603A (en) * 2019-02-18 2019-04-26 兰州交通大学 Consider the ICPT system resonance compensating parameter optimization method of signal and electric energy parallel transmission
CN110782080A (en) * 2019-10-21 2020-02-11 浙江大学 Electric power system structure optimization method based on population performance sorting selection
CN110782080B (en) * 2019-10-21 2022-05-27 浙江大学 Electric power system structure optimization method based on population performance sorting selection
CN111178528A (en) * 2019-12-20 2020-05-19 江苏方天电力技术有限公司 Elite genetic algorithm improvement method applied to wireless charging system
CN111178528B (en) * 2019-12-20 2022-06-07 江苏方天电力技术有限公司 Elite genetic algorithm improvement method applied to wireless charging system
CN111709559A (en) * 2020-05-29 2020-09-25 杭州电子科技大学 Intelligent transportation scheduling optimization method based on improved genetic algorithm
CN113301576A (en) * 2021-05-26 2021-08-24 南京邮电大学 Cellular network resource allocation method based on improved genetic algorithm
CN113806884A (en) * 2021-09-02 2021-12-17 广东泰坦智能动力有限公司 Resonant converter design parameter selection method based on genetic algorithm
CN113794286A (en) * 2021-09-06 2021-12-14 湖北工业大学 Parameter optimization method and device for wireless power transmission system

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Application publication date: 20160210