CN110996381B - Radio frequency energy source arrangement and emission power setting method based on genetic algorithm - Google Patents
Radio frequency energy source arrangement and emission power setting method based on genetic algorithm Download PDFInfo
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Abstract
A radio frequency energy source arrangement and emission power setting method based on a genetic algorithm is characterized in that for the conditions of a given node position for capturing radio frequency energy, a node energy capture required value, a radio frequency energy source candidate arrangement position and the number of energy sources to be arranged, the arrangement position of the energy sources and the emission power setting of the energy sources are optimized by utilizing the genetic algorithm and a linear programming solving method, a fitness value of a chromosome in the algorithm is defined as the total emission power of the energy sources, and the energy source arrangement position selection and the emission power setting of the energy sources with smaller network total power consumption are finally obtained by iteratively executing operations such as chromosome selection, intersection and variation. The method of the invention realizes reasonable arrangement of the arrangement position and the emission power of the radio frequency energy source and can achieve smaller total network power consumption.
Description
Technical Field
The invention relates to a radio frequency energy source arrangement and emission power setting method based on a genetic algorithm, which is suitable for a wireless sensor network with sensor nodes capable of capturing radio frequency energy.
Background
Electromagnetic waves are increasingly receiving attention from both academic and industrial circles as a ubiquitous, environmentally friendly and sustainable energy source. The radio frequency energy capturing wireless sensor network is a novel network for capturing radio frequency energy in an environment and converting the radio frequency energy into electric energy so as to support continuous work of nodes.
However, at present, the rate of capturing radio frequency energy in the environment by the radio frequency energy capturing sensor node is still very low, which is one of the bottlenecks in the wide application of this type of new networks. In order to overcome the weakness, it is a feasible and effective method to deploy a dedicated radio frequency energy source to supply power to the node and adjust the emission power of the energy source.
Since the radio frequency energy can lose a certain amount of energy in the transmission process, namely the farther the energy source is away from the node, the less radio frequency energy is captured by the node, and the energy capture power of the node depends on the arrangement position of the energy source. In addition, the node often has an energy capture power demand value, for example, the value is the average power consumption of the node, and the excess part of the actual capture power exceeding the demand value does not bring any benefit. Therefore, for the given scene of the node position for capturing the radio frequency energy, the node energy capture required value, the candidate arrangement positions of the radio frequency energy sources and the number of the energy sources to be arranged, the reasonable arrangement positions are selected from the candidate arrangement position set, the emission power of the arranged energy sources is adjusted, and the combined optimization enables the total energy supply of the energy sources to be minimized, namely the total emission power of the energy sources to be minimized, so that the method is one of the important problems to be solved by the radio frequency energy capture network.
Disclosure of Invention
In order to realize the energy source arrangement with lower network total power consumption, the invention provides a radio frequency energy source arrangement and emission power setting method based on a genetic algorithm according to the given node position for capturing the radio frequency energy, the node energy capture required value, the radio frequency energy source candidate arrangement position and the number of the energy sources to be arranged, so as to achieve lower network total power consumption.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a genetic algorithm based radio frequency energy source placement and transmit power setting method, the method comprising the steps of:
step 1. For i =1,2, …, M and j =1,2, …, N, where M is the number of nodes capturing radio frequency energy and N is the number of candidate placement positions of the radio frequency energy source, calculating the distance d between the ith node and the jth candidate placement position i，j ；
Step 2, chromosome population initialization: setting the length of the chromosome as the number N of candidate arrangement positions, wherein the jth gene of the chromosome corresponds to the jth candidate arrangement position, the jth gene value is 0 and represents that the jth candidate position does not place an energy source, and the jth gene value is 1 and represents that the jth candidate position places an energy source; generating m stainsRandomly picking out K genes from each chromosome and setting the K gene values as 1, setting the values of other N-K genes as 0,m for population scale, and setting K as the number of energy sources to be arranged; for each chromosome, calculating the total energy supply minimization subproblem under the arrangement of the corresponding energy sources to obtain the optimal transmission power of each energy source and the total transmission power P of the K energy sources min The fitness value of the chromosome is defined as p min ；
Step 3, representing the chromosome with the maximum fitness value as Ch _ best;
step 4, initializing an iteration time variable Times to be 0;
and 5, selecting: randomly selecting 2 chromosomes from m chromosomes of the current generation to be matched as a pair, and performing the operation for n times to obtain n pairs of chromosomes, wherein the value of n is more than or equal to that of the chromosome
So that the number of newly generated chromosomes in the next step is not less than m;
step 6, crossing: the following operations are performed for each of the pairs of n chromosomes Ch1 and Ch 2: splicing the first half gene of Ch1 and the second half gene of Ch2 into a new chromosome Ch3, and splicing the second half gene of Ch1 and the first half gene of Ch2 into another new chromosome Ch4; for Ch3 and Ch4, when the number L of the genes with the value of 1 is larger than K, randomly selecting the genes with the value of 1 from L-K, modifying the values of the genes into 0, when the number L of the genes with the value of 1 is smaller than K, randomly selecting the genes with the value of 0 from K-L, and modifying the values of the genes into 1;
and 7, mutation: performing mutation operation on each chromosome of the 2n chromosomes generated in the step 6 with a mutation probability of 0.5% as follows: randomly picking out one of the genes with the value of 1 to modify the value of the gene to be 0; then randomly picking out one of the genes with the value of 0 to modify the value of the gene to 1;
step 8, calculating the fitness values of the 2n chromosomes obtained in the step 7 respectively, finding out the chromosome Ch with the maximum fitness value in the 2n chromosomes, and if the fitness value of Ch is smaller than the fitness value of Ch _ best, reserving the chromosomes with fitness of m-1 in the 2n chromosomes and the chromosome Ch _ best as a new generation population; if the fitness value of Ch is greater than the fitness of Ch _ best, updating Ch _ best to Ch, and keeping the first m chromosomes with the fitness values in the 2n chromosomes as a new generation population;
step 9.Times = Times +1, if Times equals to the preset cycle iteration number H, step 10 is skipped, otherwise step 5 is skipped;
step 10, determining the arrangement of energy sources according to the gene values of the chromosome Ch _ best, namely, for j =1,2, …, N, if the jth gene value of the chromosome Ch _ best is 0, no energy source is placed at the jth candidate position, if the jth gene value is 1, one energy source is placed at the jth candidate position, and setting the transmission power of K energy sources as the optimal transmission power corresponding to the total energy supply minimization subproblem under the arrangement of the energy sources;
and 11, ending.
Further, in step 2 and step 8, the sub-problem of minimizing the total energy supply under the arrangement of the energy sources corresponding to one chromosome is calculated, and the optimal transmitting power and the total transmitting power P of each energy source are obtained min The process is as follows:
step 2.1. Using symbol I i To characterize whether a source of rf energy is placed at the ith candidate placement location: if a radio frequency energy source is deployed at the ith candidate deployment location, I i =1, otherwise I i =0; determining I according to the current arrangement positions of K energy sources 1 ，I 2 ，…，I N I.e. if one radio frequency energy source is arranged at the ith candidate arrangement position, I i =1, otherwise I i ＝0；
And 2.2, solving the following linear programming problem by using the existing algorithm for solving the linear programming problem to obtain the respective transmission power of the K energy sources: the optimization variable is P 1 ，P 2 ，…，P N In which P is j Representing the transmission power of the energy source arranged at the jth candidate position, the optimization objective being the total transmission power
The constraint being>
i =1,2, …, M, and 0 ≦ P n ≤P max N =1,2, …, N, wherein &>
Is the ith node S i Energy capture power, P, to be achieved max Is the maximum transmit power of the energy source, α is a constant determined by the energy source and the hardware and environment of the node;
step 2.3, the total transmitting power P of the K energy sources is calculated min 。
The beneficial effects of the invention are as follows: and determining the arrangement and the emission power setting of the radio frequency energy sources according to the given node position for capturing the radio frequency energy, the node energy capturing required value, the candidate arrangement positions of the radio frequency energy sources and the number of the energy sources to be arranged, so as to achieve lower total network power consumption.
Drawings
Fig. 1 is a flow chart of the radio frequency energy source placement and transmit power setting scheme of the present invention based on a genetic algorithm.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, a radio frequency energy source arrangement and transmission power setting method based on a genetic algorithm includes the following steps:
step 1. For i =1,2, …, M and j =1,2, …, N, where M is the number of nodes capturing radio frequency energy and N is the number of candidate placement positions of the radio frequency energy source, calculating the distance d between the ith node and the jth candidate placement position i，j ；
Step 2, chromosome population initialization: setting the length of the chromosome as the number N of candidate arrangement positions, wherein the jth gene of the chromosome corresponds to the jth candidate arrangement position, and the jth gene value is 0 and represents that the jth candidate position is notPlacing an energy source, wherein the j gene value is 1, and the j candidate position is placed with the energy source; generating m chromosomes, randomly picking out K genes from each chromosome and setting the K gene values to be 1, setting the other N-K gene values to be 0,m for the population scale, and setting K to be the number of energy sources to be arranged; for each chromosome, calculating the total energy supply minimization subproblem under the arrangement of the corresponding energy sources to obtain the optimal transmission power of each energy source and the total transmission power P of the K energy sources min The fitness value of the chromosome is defined as p min ；
Step 3, representing the chromosome with the maximum fitness value as Ch _ best;
step 4, initializing an iteration time variable Times to be 0;
and 5, selecting: randomly selecting 2 chromosomes from m chromosomes of the current generation to be matched as a pair, and performing the operation for n times to obtain n pairs of chromosomes, wherein the value of n is more than or equal to that of the chromosome
So that the number of newly generated chromosomes in the next step is not less than m;
step 6, crossing: the following operations were performed for each of the pairs of chromosomes Ch1 and Ch2 of the n pairs of chromosomes: splicing the first half gene of Ch1 and the second half gene of Ch2 into a new chromosome Ch3, and splicing the second half gene of Ch1 and the first half gene of Ch2 into another new chromosome Ch4; for Ch3 and Ch4, when the number L of the genes with the value of 1 is larger than K, randomly selecting the genes with the value of 1 from L-K, modifying the values of the genes into 0, when the number L of the genes with the value of 1 is smaller than K, randomly selecting the genes with the value of 0 from K-L, and modifying the values of the genes into 1;
and 7, mutation: performing mutation operation on each chromosome of the 2n chromosomes generated in the step 6 with a mutation probability of 0.5% as follows: randomly picking out one of the genes with the value of 1 to modify the value of the gene to be 0; then randomly picking out one of the genes with the value of 0 to modify the value of the gene to 1;
step 8, calculating the fitness values of the 2n chromosomes obtained in the step 7 respectively, finding out the chromosome Ch with the maximum fitness value in the 2n chromosomes, and if the fitness value of the Ch is smaller than the fitness value of Ch _ best, reserving the chromosomes with the fitness of m-1 before and the chromosomes Ch _ best in the 2n chromosomes as a new generation of population; if the fitness value of Ch is greater than the fitness of Ch _ best, updating Ch _ best to Ch, and keeping the m chromosomes with the fitness values higher than the fitness value of the 2n chromosomes as a new generation population;
step 9.Times = Times +1, if Times equals to the preset cycle iteration number H, step 10 is skipped, otherwise step 5 is skipped;
step 10, determining the arrangement of energy sources according to the gene values of the chromosome Ch _ best, namely, for j =1,2, …, N, if the jth gene value of the chromosome Ch _ best is 0, no energy source is placed in the jth candidate position, if the jth gene value is 1, an energy source is placed in the jth candidate position, and setting the transmission power of K energy sources as the optimal transmission power corresponding to the total energy supply minimization subproblem under the arrangement of the energy sources;
and 11, ending.
Further, in step 2 and step 8, the sub-problem of minimizing the total energy supply under the arrangement of the energy sources corresponding to one chromosome is calculated, and the optimal transmitting power and the total transmitting power P of each energy source are obtained min The process is as follows:
step 2.1. Using symbol I i To characterize whether a source of rf energy is placed at the ith candidate placement location: if a radio frequency energy source is deployed at the ith candidate deployment location, then I i =1, otherwise I i =0; determining I according to the current arrangement positions of K energy sources 1 ，I 2 ，…，I N I.e. if one radio frequency energy source is arranged at the ith candidate arrangement position, I i =1, otherwise I i ＝0；
And 2.2, solving the following linear programming problem by using the existing algorithm for solving the linear programming problem to obtain the respective transmission power of the K energy sources: the optimization variable is P 1 ，P 2 ，…，P N In which P is j Indicates placement at jth candidateEnergy source transmit power of a location, the optimization target being the total transmit power
The constraint being->
i =1,2, …, M, and 0 ≦ P n ≤P max N =1,2, …, N, wherein &>
Is the ith node S i Energy capture power, P, to be achieved max Is the maximum transmit power of the energy source, α is a constant determined by the energy source and the hardware and environment of the node;
step 2.3, the total transmitting power P of the K energy sources is calculated min 。
In this example. Specific embodiments of the present invention are described for this class of scenarios given a node location to capture radio frequency energy, a node energy capture demand value, a radio frequency energy source candidate placement location, and a number of energy sources to be placed.
First, the distance between each node and each candidate arrangement position is calculated according to the physical position of each node and the physical position of each candidate arrangement position.
Chromosome population initialization is then performed. Setting the length of the chromosome as the number N of candidate arrangement positions, wherein the jth gene of the chromosome corresponds to the jth candidate arrangement position, the jth gene value is 0 and represents that no energy source is arranged at the jth candidate position, and the jth gene value is 1 and represents that the energy source is arranged at the jth candidate position. Randomly generating m chromosomes, randomly picking out K genes from each chromosome and setting the K genes to be 1, setting the values of other N-K genes to be 0,m for the population scale, and setting K to be the number of energy sources to be arranged. And calculating the fitness value p of each chromosome by linear programming solution min The chromosome having the largest fitness value is denoted as Ch _ best;
selection, crossover, mutation and evolution are then performed iteratively. In each iteration, 2 chromosomes in the current generation of m chromosomes are randomly picked out to form a pair, and the operation is performed for n times to obtain n pairs of chromosomes. For n pairs of chromosomes: each pair is a group, the cross exchange of chromosomes is carried out to generate new chromosomes, and the new chromosomes are mutated. And then carrying out chromosome win-lose operation, wherein the operation is to eliminate chromosomes with low fitness value and keep m chromosomes with high fitness value to form a new population. Specifically, fitness values of the obtained 2n chromosomes are calculated respectively, a chromosome Ch with the largest fitness value in the 2n chromosomes is found, and if the fitness value of Ch is smaller than that of Ch _ best, the chromosomes with fitness being m-1 before the 2n chromosomes and the Ch _ best are reserved as a new generation population; if the fitness value of Ch is greater than the fitness of Ch _ best, updating Ch _ best to Ch, and keeping the m first chromosomes of the 2n chromosomes with fitness values as a new generation population. And continuously carrying out iterative updating on the chromosome population until a fixed iteration number is reached and ending.
After iteration is finished, the arrangement of the energy sources is determined according to the gene values of the chromosome Ch _ best, namely for j =1,2, …, N, if the jth gene value of the chromosome Ch _ best is 0, the energy source is not placed in the jth candidate position, and if the jth gene value is 1, the energy source is placed in the jth candidate position.
Claims (1)
1. A method for arranging and setting transmission power of radio frequency energy sources based on genetic algorithm, which is characterized by comprising the following steps:
step 1. For i =1,2, …, M and j =1,2, …, N, where M is the number of nodes capturing radio frequency energy and N is the number of candidate placement positions for the source of radio frequency energy, calculating the distance d between the ith node and the jth candidate placement position i，j ；
Step 2, chromosome population initialization: setting the length of the chromosome as the number N of candidate arrangement positions, wherein the jth gene of the chromosome corresponds to the jth candidate arrangement position, the jth gene value is 0 and represents that the jth candidate position does not place an energy source, and the jth gene value1 represents that the jth candidate position is provided with an energy source; generating m chromosomes, randomly picking out K genes from each chromosome and setting the K gene values to be 1, setting the other N-K gene values to be 0,m for the population scale, and setting K to be the number of energy sources to be arranged; for each chromosome, calculating the total energy supply minimization problem under the arrangement of the corresponding energy sources to obtain the optimal transmission power of each energy source and the total transmission power P of the K energy sources min The fitness value of the chromosome is defined as p min ；
Step 3, representing the chromosome with the maximum fitness value as Ch _ best;
step 4, initializing an iteration time variable Times to be 0;
and 5, selecting: randomly selecting 2 chromosomes in the m chromosomes of the current generation to be matched into a pair, and operating n times to obtain n pairs of chromosomes, wherein the value of n is more than or equal to
So that the number of newly generated chromosomes in the next step is not less than m;
step 6, crossing: the following operations are performed for each of the pairs of n chromosomes Ch1 and Ch 2: splicing the first half gene of Ch1 and the second half gene of Ch2 into a new chromosome Ch3, and splicing the second half gene of Ch1 and the first half gene of Ch2 into another new chromosome Ch4; for Ch3 and Ch4, when the number L of the genes with the value of 1 is larger than K, randomly selecting the genes with the value of L-K being 1, modifying the values of the genes to be 0, when the number L of the genes with the value of 1 is smaller than K, randomly selecting the genes with the value of K-L being 0, and modifying the values of the genes to be 1;
and 7, mutation: performing mutation operation on each chromosome of the 2n chromosomes generated in the step 6 with a mutation probability of 0.5% as follows: randomly picking out one of the genes with the value of 1 to modify the value of the gene to be 0; then randomly picking out one of the genes with the value of 0 to modify the value of the gene to 1;
step 8, calculating the fitness values of the 2n chromosomes obtained in the step 7 respectively, finding out the chromosome Ch with the maximum fitness value in the 2n chromosomes, and if the fitness value of Ch is smaller than the fitness value of Ch _ best, reserving the chromosomes with fitness of m-1 in the 2n chromosomes and the chromosome Ch _ best as a new generation population; if the fitness value of Ch is greater than the fitness of Ch _ best, updating Ch _ best to Ch, and keeping the m chromosomes with the fitness values higher than the fitness value of the 2n chromosomes as a new generation population;
step 9.Times = Times +1, if Times equals to the preset cycle iteration number H, step 10 is skipped, otherwise step 5 is skipped;
step 10, determining the arrangement of energy sources according to the gene values of the chromosome Ch _ best, namely, for j =1,2, …, N, if the jth gene value of the chromosome Ch _ best is 0, no energy source is placed at the jth candidate position, if the jth gene value is 1, one energy source is placed at the jth candidate position, and setting the transmission power of K energy sources as the optimal transmission power corresponding to the total energy supply minimization subproblem under the arrangement of the energy sources;
step 11, ending;
in the step 2 and the step 8, the problem of minimizing the total energy supply under the arrangement of the energy sources corresponding to one chromosome is calculated to obtain the optimal transmitting power and the total transmitting power P of each energy source min The process is as follows:
step 2.1. Using symbol I i To characterize whether a source of rf energy is placed at the ith candidate placement location: if a radio frequency energy source is deployed at the ith candidate deployment location, then I i =1, otherwise I i =0; determining I according to the current arrangement positions of K energy sources 1 ，I 2 ，…，I N I.e. if one radio frequency energy source is arranged at the ith candidate arrangement position, I i =1, otherwise I i ＝0；
And 2.2, solving the following linear programming problem by using the existing algorithm for solving the linear programming problem to obtain the respective transmission power of the K energy sources: the optimization variable is P 1 ，P 2 ，…，P N In which P is j Representing the transmission power of the energy source arranged at the jth candidate position, the optimization objective being the total transmission power
The constraint being->
i =1,2, …, M, and 0 ≦ P n ≤P max N =1,2, …, N, wherein->
Is the ith node S i Energy capture power, P, to be achieved max Is the maximum transmit power of the energy source, α is a constant determined by the energy source and the hardware and environment of the node;
step 2.3, the total transmitting power P of the K energy sources is calculated min 。
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