CN103916927B - A kind of wireless sensor network routing method based on improvement harmonic search algorithm - Google Patents
A kind of wireless sensor network routing method based on improvement harmonic search algorithm Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The present invention proposes a kind of wireless sensor network routing method based on improvement harmonic search algorithm, comprises the following steps：Step1, initialization algorithm relevant parameter HMS, HMCR, PAR and evaluation number of times eval_Nomax；Step2, using roulette initialization harmony data base HM；Step3, the fitness for evaluating each and acoustic path in harmony storehouse；Step4, setting eval_No=0；Step5, setting i=0；Step6, generation candidate's harmony；Step7, eval_No++, if eval_No ＜ eval_Nomax, perform Step8；Otherwise perform Step11；Step8, in harmony storehouse i-th harmony Xi=s, x2 ... xj ..., d }, carry out neighborhood search；Step9, eval_No++, if eval_No ＜ eval_Nomax, perform Step10；Otherwise perform Step11；Step10, i++, if i ＜ HMS, perform Step6；Otherwise perform Step5；Optimal and acoustic path in Step11, record harmony data base.Method for routing of the invention has efficiency higher, and can effectively extend the life cycle of whole network.
Description
Technical field
The invention belongs to wireless sensor network technology field, and in particular to a kind of route side of improvement harmonic search algorithm
Method.
Background technology
Wireless sensor network（Wireless Sensor Network, WSN）With traditional internet and recent years
The swift and violent wireless self-networking of development（MobileAd Hoc Network）Suffer from difference largely.In traditional Yin Te
In net and Ad Hoc networks, each network node is under normal circumstances a PC or mobile device, user use because
Special net and Ad Hoc nets are primarily used to obtain network share information, so either internet or AdHoc networks, route association
The purpose of design is discussed primarily to improving service quality, without user's consumption how many energy considered.Radio sensing network is then
Difference, radio sensing network is in detection zone（Environment very severe or people cannot reach under normal circumstances）Inside disseminate a large amount of
Microsensor node, the ID that these sensing nodes are not fixed, a but sensing is constituted by way of self-organizing
Device network.Wireless sensor network just obtains the favor of various circles of society once appearance, applies in military affairs, medical treatment, and environment etc. is each
Individual field.
Routing algorithm is played an important role in wireless sensor network, its energy consumption to each node, whole network
Life cycle and communication quality play critical effect.Therefore, the research of routing algorithm is received more and more attention.By
There is energy constraint in sensor network, the features such as resource-constrained, topologies change is frequent, therefore, traditional routing mechanism
Do not adapt to radio sensing network, it is necessary to design corresponding routing algorithm.
So far, the Routing Protocol for wireless sensor network mainly includes：Plane Routing Protocol（Flooding agreements,
SPIN agreements, MTE agreements, Directed Diffusion agreements）, hierarchical routing（LEACH agreements）, based on positional information
Routing Protocol（GAF agreements, GEAR agreements）And the Routing Protocol based on data flow and QoS.
At present, the method for routing based on intelligent optimization algorithm there has also been some and answers in wireless sensor network technology field
With being mainly based upon ant group algorithm（Ant Colony Optimization,ACO）Method for routing and based on ant colony algorithm
（Bee Colony Optimization,BCO）Method for routing.
The research of radio sensing network Routing Protocol has been increasingly becoming focus, and the research of Routing Protocol is from simple to again
It is miscellaneous, from data-centered to high-quality requirement, and towards intelligent direction development, how to set up the efficient, sensing of self adaptation
Routing algorithm turns into a focus of current research.
Harmonic search algorithm is a kind of novel intelligent optimization algorithm that South Korea scholar Geem in 2001 et al. is proposed.Algorithm
Musicians, by adjusting the tone of each musical instrument in band repeatedly, are finally reached by the memory of oneself in simulating musical performance
One process of beautiful harmony state.Harmonic search algorithm has very strong ability of searching optimum, it is easy to converge to global optimum
Solution, its flow chart are as shown in figure 1, execution step is as follows：
Step1：Initialization algorithm relevant parameter.
Initialization HMS（The size of harmony data base, i.e. population amount of capacity）、HMCR（Select probability）、PAR（Adjustment is general
Rate）、BW（Adjustment bandwidth）, evaluate number of times eval_Nomax。
Step2：Harmony data base（Harmony Memory,HM）Initialization.
In this step, formula（1）Shown HM byIt is interior to produce one group of random number to initialize, wherein 1≤
i≤n.So as to according to formula（2）Obtain j-th i-th variate-value of solution vector：
In above formula, j=1,2 ..., HMS, Random (0,1) are the random numbers between 0 to 1.
Step3：Calculate the fitness of each harmony in harmony storehouse.
Step4：Eval_No=0 is set.
Step5：Extemporize candidate's harmony.
Step5.1：In this step, a new harmony vectorProduced by three rules
It is raw：
1. memory selection；
2. randomly choose；
3. pitch adjustment.
Produce candidate's harmony to be referred to as extemporize, 1. and 2. first pass through rule and determine be memory selection or with
Machine is selected, specific as follows shown：
In formula, P1It is the random number between 0 to 1, xrand(i),jRepresent and one point is randomly choosed in the jth row component of HM
Amount, ΩjRepresent j-th domain of definition of component.
Step5.2：Each will be tested further by the tone that memory selection is obtained and decide whether that pitch is adjusted,
This operates with PAR parameters, and pitch adjustment decision-making is as follows：
In formula, P2It is the random number between 0 to 1.
Step5.3：Calculate the fitness value f (π) of candidate's harmony；
Step5.4：Update HM.
According to target function value, if candidate's harmony vector is better than vector worst in HM, in new vector substitution HM most
Poor harmony vector, does not operate otherwise.
Step6：Check whether stopping iteration.
Eval_No++, if eval_No ＜ eval_Nomax, perform Step5；Otherwise perform Step7.
Step7：Optimal solution in record harmony storehouse.
However, traditional harmonic search algorithm cannot be directly used to wireless sensor network route.
The content of the invention
In consideration of it, a kind of based on the wireless sensor network road for improving harmonic search algorithm it is an object of the invention to propose
By method, it is improved by path fitness model so that the agreement is in efficiency and extends whole wireless sensor network
Network life-span aspect has great superiority.
The present invention uses following technical scheme to realize foregoing invention purpose：
A kind of wireless sensor network routing method based on improvement harmonic search algorithm, comprises the following steps：
Step1, initialization algorithm relevant parameter HMS, HMCR, PAR and evaluation number of times eval_Nomax；
Step2, using roulette initialization harmony data base HM；
Each harmony in Step3, calculating harmony storehouse（Path）Fitness；
Step4, setting eval_No=0；
Step5, setting i=0；
Step6, generation candidate's harmony simultaneously update harmony data base；
Step7, eval_No++, if eval_No ＜ eval_Nomax, perform Step8；Otherwise perform Step11；
Step8, to i-th harmony Xi={ s, x in harmony storehouse2,…,xj..., d } carry out neighborhood search；
Step9, eval_No++, if eval_No ＜ eval_Nomax, perform Step10；Otherwise perform Step11；
Step10, i++, if i ＜ HMS, perform Step6；Otherwise perform Step5；
Optimal harmony in Step11, record harmony data base（Path）.
Compared with prior art, the invention has the advantages that：Routing algorithm has efficiency higher, and can prolong
The life cycle of whole network long.Technique effect of the invention will also obtain further body in the elaboration of specific embodiment
It is existing.
Brief description of the drawings
Fig. 1 is tradition HS algorithm flow charts；
Fig. 2 is the flow chart of improved harmonic search algorithm solution WSN optimal paths in the embodiment of the present invention；
Fig. 3 is the path candidate product process figure of improvement harmonic search algorithm in the embodiment of the present invention；
Fig. 4 is the Path neighborhood search routine figure of improvement harmonic search algorithm in the embodiment of the present invention；
Fig. 5 is harmony library initialization schematic diagram in the embodiment of the present invention；
Fig. 6 is candidate's harmony generation schematic diagram in the embodiment of the present invention；
Fig. 7 is path neighborhood search schematic diagram in the embodiment of the present invention；
Fig. 8 is 100 WSN scenes of node of random generation in the embodiment of the present invention；
Fig. 9 is the improvement harmonic search algorithm of the embodiment of the present invention（IHS）With the convergence feelings of ant group algorithm average fitness
Condition is contrasted.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below with reference to the accompanying drawings and with reference to example in detail
Describe the bright present invention in detail.
The present invention is improved the characteristics of route for wireless sensor network to traditional harmony searching algorithm, improve and
Sound searching algorithm carries out the flow chart of WSN routes as shown in Fig. 2 specifically including herein below：
（1）The initialization of harmony data base
In traditional harmony searching algorithm, every harmony in harmony data base necessarily requires dimension identical, such as formula（1）Institute
Show.In embodiments of the present invention, every harmony in harmony data base is produced by roulette, and their dimension can not
Identical, the head and the tail element of every harmony is respectively source node and aggregation node, HM such as formulas（5）It is shown.
In formula, behalf source node numbering, d represents aggregation node numbering, xi,jRepresent that other sensor nodes are numbered.
As can be seen from the above equation, the length of each harmony in harmony storehouse（That is dimension）May differ.
（2）The generation of candidate's harmony
Candidate's harmonyGeneration as shown in figure 3, wherein：
In formula,Represent in nodeCommunication range interior nodes set, { x1,j,x2,j,…,xHMS,jTable
Show the jth row in harmony storehouse, P1It is the random number between 0 to 1, xrand(i),jRepresent and one is randomly choosed in the jth row component of HM
Component,Represent in nodeCommunication range in random selection one node.
As random number P1Less than harmonic search algorithm select probability HMCR when, the next-hop of candidate's harmony from and sound memory
Selected in storehouse；Otherwise, the node not reached is selected from the communication range of present node at random as next-hop.
If next-hop node takes from harmony data base, need to judge whether it needs adjustment：As random number P2It is less than
Adjustment probability P AR when, be adjusted to taking from the tone in harmony storehouse, selected from the communication range of present node at random not to
Selected node is replaced as next-hop up to the node crossed, and otherwise, keeps selected node as next-hop.If under
One hop node only takes from the communication context of present node, unrelated with harmony storehouse, then without adjustment.By that analogy, until reaching remittance
Poly- node.
Wherein, to candidate's harmonyIn to take from the adjustment that the tone in harmony storehouse carries out as follows：
In formula, P2It is the random number between 0 to 1.
（3）Neighborhood search
As shown in figure 4, to i-th harmony X in harmony storehousei={ s, x2,…,xj..., d } carry out neighborhood search, mathematics
Model is as follows：
The field searching method can significantly improve convergence of algorithm speed.
（4）The fitness function model in path
In the embodiment of the present invention, the path fitness function model such as formula of wireless sensor network route（9）It is shown, use
The model can make the routing algorithm have an efficiency higher, and can extend the life cycle of whole network, especially for each node
The situation that initial total energy is differed.
In formula, molecule is radio energy consumption model, and denominator is the dump energy index of path interior joint.L is the length in path
Degree, EelecIt is the unit energy consumption transmitted and receive, value is 50nJ/bit, EampIt is the unit energy consumption that transmission is amplified, value is
100pJ/(bit*m2), k is the data package size that source node sends, di,i+1Represent between i-th node and i+1 node
Distance, EMinThe dump energy of the minimum node of dump energy, E in expression pathAvgThe average of all nodes remains in representing path
Complementary energy.
Specifically, the method for the embodiment of the present invention is comprised the following steps：
Step1, initialization algorithm relevant parameter HMS, HMCR, PAR and evaluation number of times eval_Nomax。
Step2, using roulette initialization harmony data base HM.
When initializing harmony data base using roulette, select probability P (i, j) of the node j in node i communication range is such as
Shown in lower：
In formula, hopjRepresent the hop count of node j, hopkRepresent the hop count of node k, hopmaxRepresent the hop count in all nodes
The hop count of maximum node, EjRepresent the dump energy of node j, EkRepresent the dump energy of node k, allowediRepresenting can be with
The node set of the next-hop as node i, No (allowedi) represent set allowediNumber of elements.
By the select probability, initial path can be made with larger probability selection near aggregation node and dump energy
More node, the initialization schematic diagram of harmony data base is as shown in Figure 5.
Each harmony in Step3, calculating harmony storehouse（Path）Fitness f (π).
Step4, setting eval_No=0.
Step5, setting i=0.
Step6, generation candidate's harmony simultaneously update harmony data base.
The generation schematic diagram of candidate's harmony is as shown in fig. 6, from fig. 6, it can be seen that first node of candidate's harmony is source
Node, during selection next-hop, r1＜ HMCR, and r2＞ PAR, therefore, algorithm is at random from the second column selection source node in harmony storehouse
Node in communication range（Here example selection node 31）As next-hop；When selecting the 3rd node, r1＞ HMCR, because
This, algorithm selects a node not reached from the communication range of node 31 at random（Here example selection 33）As
Next-hop；The rest may be inferred, can obtain candidate's harmony as shown in Figure 6.
Step6.1, the fitness value f (π) for calculating candidate's harmony；
Step6.2, harmony worst in candidate's harmony and HM is compared, if being better than the worst harmony, should
Worst harmony replaces out harmony data base.
Step7, eval_No++, if eval_No ＜ eval_Nomax, perform Step8；Otherwise perform Step11.
Step8, to i-th harmony X in harmony storehousei={ s, x2,…,xj..., d } carry out neighborhood search.
Neighborhood search schematic diagram as shown in fig. 7, by randomly choosing the node in path, selected node upper hop and
One node not reached of random selection replaces selected node in the communication range common factor of next-hop, so as to complete neighborhood search
Rope.For the path in figure, have selected the 71st node carries out neighborhood search, it can be seen that the upper hop of node 71
The communication range of node 33 and next-hop node 80 occurs simultaneously and includes node 63,64,65,71,82, therefore, algorithm is at random therefrom
One node of selection（Here example selection node 63）Substitute node 71 completes neighborhood search.
Step8.1, the fitness value f (π) for calculating the harmony that neighborhood search is obtained.
Step8.2, the harmony for obtaining neighborhood search are compared with the harmony before carrying out neighborhood search, if be better than
Harmony before, then replace out harmony data base by harmony before.
Step9, eval_No++, if eval_No ＜ eval_Nomax, perform Step10；Otherwise perform Step11.
Step10, i++, if i ＜ HMS, perform Step6；Otherwise perform Step5.
Optimal harmony in Step11, record harmony data base（Path）.
Effect of the invention can further be verified by following emulation experiment and relatively：
The WSN scenes shown in Fig. 8 are chosen, by the improvement harmonic search algorithm in the present invention（IHS）With ant group algorithm（ACO）
It is compared.Programming language is C++, and allocation of computer is：Intel I7-3610QM processors, 8GB internal memories, 2GB solely show,
The notebook computer of windows764 bit manipulation systems.The abscissa and ordinate of each node are respectively such as Tables 1 and 2 institute in scene
Show, the initial total energy of each node is as shown in table 3.
The abscissa of each nodes of table 1WSN
| No | X-axis coordinate | No | X-axis coordinate | No | X-axis coordinate | No | X-axis coordinate | No | X-axis coordinate |
| 1 | 1038.063699 | 21 | 288.486155 | 41 | 93.880562 | 61 | 108.570707 | 81 | 332.141475 |
| 2 | 470.015997 | 22 | 1070.806875 | 42 | 1000.758772 | 62 | 350.735654 | 82 | 434.161440 |
| 3 | 796.022940 | 23 | 663.764978 | 43 | 684.108914 | 63 | 260.472487 | 83 | 877.499586 |
| 4 | 760.394988 | 24 | 141.228770 | 44 | 130.028695 | 64 | 581.335254 | 84 | 927.195665 |
| 5 | 451.998943 | 25 | 950.243855 | 45 | 1032.180685 | 65 | 918.536073 | 85 | 757.365042 |
| 6 | 73.157485 | 26 | 700.444053 | 46 | 80.002362 | 66 | 1014.104825 | 86 | 510.760722 |
| 7 | 268.720797 | 27 | 551.871345 | 47 | 70.394198 | 67 | 807.038443 | 87 | 697.026192 |
| 8 | 18.310381 | 28 | 200.715905 | 48 | 810.720847 | 68 | 360.427584 | 88 | 1050.648601 |
| 9 | 506.471469 | 29 | 40.233365 | 49 | 370.548313 | 69 | 322.964728 | 89 | 181.477631 |
| 10 | 830.160846 | 30 | 232.451389 | 50 | 500.487349 | 70 | 772.977461 | 90 | 657.995492 |
| 11 | 225.383031 | 31 | 402.776618 | 51 | 847.995887 | 71 | 958.541697 | 91 | 746.170122 |
| 12 | 586.077114 | 32 | 674.172879 | 52 | 54.363618 | 72 | 1060.103491 | 92 | 676.354322 |
| 13 | 180.533438 | 33 | 1083.709733 | 53 | 900.195729 | 73 | 100.013495 | 93 | 335.769779 |
| 14 | 316.659651 | 34 | 178.979018 | 54 | 785.705198 | 74 | 543.693628 | 94 | 407.495452 |
| 15 | 615.102143 | 35 | 760.686848 | 55 | 499.784261 | 75 | 119.200772 | 95 | 164.825008 |
| 16 | 825.980727 | 36 | 1004.310202 | 56 | 617.938018 | 76 | 276.033431 | 96 | 880.467342 |
| 17 | 936.457298 | 37 | 518.066244 | 57 | 450.612913 | 77 | 978.149229 | 97 | 560.717385 |
| 18 | 191.847072 | 38 | 150.599881 | 58 | 200.698715 | 78 | 172.276119 | 98 | 374.317783 |
| 19 | 467.889095 | 39 | 850.394587 | 59 | 610.976658 | 79 | 103.613207 | 99 | 815.726448 |
| 20 | 966.266690 | 40 | 500.090378 | 60 | 1025.537819 | 80 | 599.772413 | 100 | 236.224196 |
The ordinate of each nodes of table 2WSN
| No | Y-axis coordinate | No | Y-axis coordinate | No | Y-axis coordinate | No | Y-axis coordinate | No | Y-axis coordinate |
| 1 | 881.514269 | 21 | 199.332398 | 41 | 321.105262 | 61 | 426.254537 | 81 | 1010.474719 |
| 2 | 900.139546 | 22 | 1069.791995 | 42 | 700.612919 | 62 | 424.629072 | 82 | 650.299109 |
| 3 | 899.920884 | 23 | 801.707617 | 43 | 358.620706 | 63 | 412.729792 | 83 | 946.840883 |
| 4 | 410.732085 | 24 | 200.400755 | 44 | 125.729568 | 64 | 433.350737 | 84 | 450.859689 |
| 5 | 385.646460 | 25 | 911.142851 | 45 | 978.116910 | 65 | 682.606005 | 85 | 677.851248 |
| 6 | 66.067060 | 26 | 900.857773 | 46 | 740.034856 | 66 | 1063.605136 | 86 | 185.154071 |
| 7 | 913.267222 | 27 | 1000.791881 | 47 | 801.882039 | 67 | 226.182588 | 87 | 995.057549 |
| 8 | 482.812712 | 28 | 680.135671 | 48 | 805.298556 | 68 | 343.500789 | 88 | 788.639293 |
| 9 | 614.032108 | 29 | 595.278889 | 49 | 910.798429 | 69 | 288.815268 | 89 | 871.881713 |
| 10 | 721.787934 | 30 | 100.733508 | 50 | 460.712389 | 70 | 303.128055 | 90 | 115.568055 |
| 11 | 1000.455846 | 31 | 501.791736 | 51 | 101.356461 | 71 | 186.416999 | 91 | 770.734480 |
| 12 | 560.215866 | 32 | 580.014018 | 52 | 374.851807 | 72 | 20.033960 | 92 | 496.312489 |
| 13 | 775.619619 | 33 | 111.453285 | 53 | 863.584556 | 73 | 525.222154 | 93 | 111.029792 |
| 14 | 650.651168 | 34 | 486.843473 | 54 | 133.825872 | 74 | 267.788594 | 94 | 212.913741 |
| 15 | 952.032544 | 35 | 545.073400 | 55 | 339.555109 | 75 | 945.644309 | 95 | 350.252580 |
| 16 | 465.951681 | 36 | 150.193973 | 56 | 1047.642382 | 76 | 521.872265 | 96 | 250.712968 |
| 17 | 126.031318 | 37 | 693.612356 | 57 | 750.816902 | 77 | 982.082183 | 97 | 791.564358 |
| 18 | 166.525170 | 38 | 50.084703 | 58 | 272.840217 | 78 | 1065.785511 | 98 | 830.837048 |
| 19 | 253.999471 | 39 | 350.309441 | 59 | 710.146587 | 79 | 646.616879 | 99 | 615.348936 |
| 20 | 780.701144 | 40 | 531.949780 | 60 | 90.656761 | 80 | 179.078778 | 100 | 583.095624 |
The primary power of each nodes of table 3WSN（Unit：J）
| No | Primary power | No | Primary power | No | Primary power | No | Primary power | No | Primary power |
| 1 | 189.099 | 21 | 100.381 | 41 | 151.735 | 61 | 119.724 | 81 | 155.074 |
| 2 | 129.258 | 22 | 153.197 | 42 | 102.072 | 62 | 199.359 | 82 | 140.593 |
| 3 | 164.165 | 23 | 187.918 | 43 | 179.553 | 63 | 159.133 | 83 | 146.913 |
| 4 | 156.905 | 24 | 192.096 | 44 | 161.199 | 64 | 182.574 | 84 | 124.812 |
| 5 | 145.439 | 25 | 141.026 | 45 | 125.343 | 65 | 157.619 | 85 | 194.046 |
| 6 | 152.181 | 26 | 100.421 | 46 | 101.685 | 66 | 185.379 | 86 | 147.032 |
| 7 | 147.108 | 27 | 181.948 | 47 | 128.184 | 67 | 126.374 | 87 | 147.581 |
| 8 | 170.327 | 28 | 173.424 | 48 | 146.522 | 68 | 131.690 | 88 | 192.737 |
| 9 | 166.277 | 29 | 195.279 | 49 | 154.796 | 69 | 177.456 | 89 | 190.847 |
| 10 | 162.810 | 30 | 177.532 | 50 | 119.761 | 70 | 165.133 | 90 | 123.875 |
| 11 | 126.447 | 31 | 127.323 | 51 | 166.588 | 71 | 146.883 | 91 | 108.466 |
| 12 | 106.641 | 32 | 118.311 | 52 | 117.698 | 72 | 158.251 | 92 | 156.359 |
| 13 | 110.291 | 33 | 184.161 | 53 | 189.349 | 73 | 131.391 | 93 | 199.634 |
| 14 | 143.953 | 34 | 140.440 | 54 | 150.172 | 74 | 108.939 | 94 | 183.139 |
| 15 | 127.235 | 35 | 149.321 | 55 | 138.179 | 75 | 179.272 | 95 | 120.127 |
| 16 | 120.252 | 36 | 121.894 | 56 | 119.306 | 76 | 127.662 | 96 | 114.154 |
| 17 | 175.655 | 37 | 159.423 | 57 | 120.710 | 77 | 149.406 | 97 | 164.483 |
| 18 | 166.973 | 38 | 172.442 | 58 | 191.479 | 78 | 198.212 | 98 | 124.055 |
| 19 | 187.793 | 39 | 132.496 | 59 | 176.263 | 79 | 115.293 | 99 | 140.373 |
| 20 | 189.001 | 40 | 177.221 | 60 | 114.594 | 80 | 106.696 | 100 | 176.168 |
The node select probability p of ant group algorithmk(r, s) is as follows：
In formula, T (r, s) is the pheromones on path (r, s), and E (s) is the dump energy of node s, MkIt is the taboo of ant k
Avoid table.
Pheromone update model is as follows：
In formula, EMinkIt is the energy of the minimum node of dump energy in kth paths, EAvgkIt is kth paths interior joint
Average residual energy, FdkIt is the number of nodes of kth paths.
The Pheromone update model had both considered the life cycle that efficiency have also contemplated that whole network, for ant group algorithm,
The effect of gained is preferable.
For the WSN scenes shown in Fig. 8, the relative parameters setting such as institute of table 4 of harmonic search algorithm and ant group algorithm is improved
Show, in table, the algorithm parameter of two kinds of algorithms is the nearly figure of merit；The fitness function model such as formula in path（9）It is shown.
4 two kinds of relative parameters settings of algorithm of table
In Fig. 8, the node that five-pointed star is represented is source node, and the node that inverted triangle is represented is aggregation node.Two kinds of algorithms
Operation result compares as shown in figure 9, transverse axis represents that iterations, the longitudinal axis are often fitted after representing 50 independent operatings for the average of population
Response average.
From fig. 9, it can be seen that the effect of IHS algorithms will be significantly better than the effect of ACO algorithms, the path that IHS algorithms are obtained
It is more energy efficient, and the dump energy of path interior joint is more.
Optimal path fitness obtained by IHS algorithms is 8.88593e-006, and the energy that path is consumed is 0.136J, road
The hop count in footpath is 15, and corresponding optimal path is：6→44→18→21→69→68→5→50→64→92→35→99→
10→65→20→88。
Optimal path fitness obtained by ACO algorithms is 9.27144e-006, and the energy that path is consumed is 0.143J, road
The hop count in footpath is 17, and corresponding optimal path is：6→38→30→93→21→69→68→5→50→64→92→35→
99→10→65→20→42→88。
Therefore, the optimal path obtained by IHS algorithms can save 4.9% energy compared with the optimal path obtained by ACO algorithms
Amount, and can reduce by 11.8% hop count, propagation delay time is smaller.
IHS algorithms are more than demonstrated in the case where source node is specified, more preferable path can be found than ACO algorithm.In order to
Compare performance situation of two kinds of algorithms in whole network life cycle, the WSN scenes shown in Fig. 8 are entered respectively using two kinds of algorithms
By circulating, as shown in table 3, relative parameters setting as shown in table 5, records first to the initial total energy of each node respectively for walking along the street
The periodicity that node death is experienced, as a result as shown in table 6.
5 two kinds of relative parameters settings of algorithm of table
Comparing of the 6 two kinds of algorithms of table in WSN life cycles
As can be seen from Table 6, compared with being route using ACO algorithms, carrying out route using IHS algorithms can make whole WSN's
Life cycle extension 40.8%.Therefore, IHS algorithms are better than ACO algorithms.
Presently preferred embodiments of the present invention is the foregoing is only, is not intended to limit the invention, it is all in essence of the invention
Any modification, equivalent and improvement made within god and principle etc., should be included within the scope of the present invention.
Claims (7)
1. a kind of based on the wireless sensor network routing method for improving harmonic search algorithm, comprise the following steps：
The maximum eval_No of Step 1, the size HMS of initialization harmony data base and evaluation number of timesmax；
Step 2, using roulette initialize harmony data base HM, specifically, every harmony in harmony data base be by wheel
Disk gambling is produced, and its dimension can be differed, and the head and the tail element of every harmony in harmony data base is respectively source node and remittance
Poly- node, HM is as follows for harmony data base：
In formula, XiI-th harmony is represented, behalf source node numbering, d represents aggregation node numbering, xi,jRepresent other sensor sections
Point numbering；
Step 3, the fitness f (π) for calculating each harmony in harmony data base, wherein each harmony is in sensor network route
Path；
Number of times eval_No=0 is evaluated in Step 4, setting；
Step 5, setting i=0；
Step 6, generation candidate's harmony simultaneously update harmony data base；
Step 7, eval_No++, if eval_No ＜ eval_Nomax, perform Step 8；Otherwise perform Step 11；
Step 8, to i-th harmony X in harmony data basei={ s, x2,…,xj..., d } carry out neighborhood search, i.e., by with
Node in machine selection path, random selection one is not arrived within the upper hop of selected node and the communication range of next-hop are occured simultaneously
Selected node is replaced up to the node crossed, so as to complete neighborhood search, is specifically included：
Step 8.1, the fitness f (π) for calculating the harmony that neighborhood search is obtained；
Step 8.2, the harmony for obtaining neighborhood search are compared with the harmony before carrying out neighborhood search, if being better than it
Preceding harmony, then replace out harmony data base by harmony before；
And
Wherein, Neib (xj-1) represent in node xj-1Communication range interior nodes set, Neib (xj+1) represent in node xj+1
Communication range interior nodes set, xj∈Neib(xj-1) represent in node xj-1Communication range in random selection one section
Point；
Step 9, eval_No++, if eval_No ＜ eval_Nomax, perform Step 10；Otherwise perform Step 11；
Step 10, i++, if i ＜ HMS, performs Step 6；Otherwise perform Step 5；
Optimal and acoustic path in Step 11, record harmony data base.
2. method according to claim 1, in the Step 2, the select probability P of the node j in node i communication range
(i, j) is as follows：
In formula, hopjRepresent the hop count of node j, hopkRepresent the hop count of node k, hopmaxRepresent that the hop count in all nodes is maximum
Node hop count, EjRepresent the dump energy of node j, EkRepresent the dump energy of node k, allowediExpression can turn into
The node set of the next-hop of node i, No (allowedi) represent set allowediNumber of elements.
3. method according to claim 1, in the Step 6, candidate's harmony X'={ s, the x'2,…,x'j,…,
D }, wherein：
In formula, behalf source node numbering, d represents aggregation node numbering, Neib (x'j-1) represent in node x'j-1Communication range
The set of interior nodes, { x1,j,x2,j,…,xHMS,jRepresent that the jth of harmony data base is arranged, P1It is the random number between 0 to 1, HMCR
It is select probability, xrand(i),jExpression randomly chooses one-component, x' in the jth row component of HMj∈Neib(x'j-1) represent
Node x'j-1Communication range in random selection one node.
4. method according to claim 3, in the Step 6：
As random number P1Less than harmonic search algorithm select probability HMCR when, the next-hop of candidate's harmony is from harmony data base
Selection；Otherwise, the node not reached is selected from the communication range of present node at random as next-hop；
If next-hop node takes from harmony data base, whether the node i.e. tone that harmony data base is taken from judgement needs to adjust
It is whole：As random number P2During less than adjustment probability P AR, it is adjusted to taking from the tone in harmony data base, at random from working as prosthomere
The node that selection was not reached in the communication range of point replaces selected node as next-hop, otherwise, keeps being chosen
Node as next-hop；Until reaching aggregation node, wherein, P2It is the random number between 0 to 1.
5. method according to claim 4, the described pair of tone taken from harmony data base is adjusted specially：
6. method according to claim 1, the Step 6 includes：
Step 6.1, the fitness f (π) for calculating candidate's harmony；
Step 6.2, harmony worst in candidate's harmony and HM is compared, it is if being better than the worst harmony, this is worst
Harmony replaces out harmony data base.
7. method according to claim 1, the fitness is：
In formula, L is the length in path, EelecIt is the unit energy consumption transmitted and receive, EampIt is the unit energy consumption that transmission is amplified, k is
The data package size that source node sends, di,i+1Represent the distance between i-th node and i+1 node, EMinIn expression path
The dump energy of the minimum node of dump energy, EAvgRepresent the average residual energy of all nodes in path.
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| CN106447026B (en) * | 2016-09-13 | 2018-12-11 | 哈尔滨工程大学 | A kind of iir digital filter generation method based on cuckoo harmony search mechanisms |
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