CN105205536B - 1553B bus message transmission optimization methods based on genetic algorithm - Google Patents

1553B bus message transmission optimization methods based on genetic algorithm Download PDF

Info

Publication number
CN105205536B
CN105205536B CN201510758135.9A CN201510758135A CN105205536B CN 105205536 B CN105205536 B CN 105205536B CN 201510758135 A CN201510758135 A CN 201510758135A CN 105205536 B CN105205536 B CN 105205536B
Authority
CN
China
Prior art keywords
mrow
message
bus
chromosome
cycle
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201510758135.9A
Other languages
Chinese (zh)
Other versions
CN105205536A (en
Inventor
赵昶宇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tianjin Jinhang Computing Technology Research Institute
Original Assignee
Tianjin Jinhang Computing Technology Research Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tianjin Jinhang Computing Technology Research Institute filed Critical Tianjin Jinhang Computing Technology Research Institute
Priority to CN201510758135.9A priority Critical patent/CN105205536B/en
Publication of CN105205536A publication Critical patent/CN105205536A/en
Application granted granted Critical
Publication of CN105205536B publication Critical patent/CN105205536B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Landscapes

  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The present invention relates to a kind of 1553B bus message transmission optimization methods based on genetic algorithm, belong to bus message transmission technique field.The present invention first passes through queueing theory and establishes 1553B bus message scheduling mathematic models, it is subsequently introduced genetic algorithm and is quickly found out 1553B bus messages scheduling feasible solution, then feasible solution genetic algorithm found is converted into ant colony optimization algorithm initial information element, finally obtains 1553B bus message scheduling optimum results using the local optimal searching and positive feedback mechanism of ant group algorithm.The simulation experiment result shows, the transmission of 1553B bus messages is optimized using the genetic algorithm after improvement, can be on the premise of every message maximum delay time requirement and Realtime Capability of Communication be met, improve bus utilization, effectively alleviate bus message congestion and saturated phenomenon, solves the balanced problem of bus load, the ability with preferably processing asynchronous message.

Description

1553B bus message transmission optimization methods based on genetic algorithm
Technical field
The present invention relates to bus message transmission technique field, and in particular to a kind of 1553B based on genetic algorithm is total Line message transmission optimization method.
Background technology
1553B buses be a kind of centerized fusion interior of aircraft electronic system networking standard, its high reliability, reality When property and flexibility make it be widely used on the fields such as Aeronautics and Astronautics.
Because existing electronic system has very high requirement to real-time and reliability, it is necessary to assure disappear in 1553B buses Cease the real-time of transmission.When needing to handle a variety of message of different length different cycles in 1553B buses, and exist asynchronous When message needs processing, the real-time of system it is general it is difficult to ensure that.1553B bus message optimized algorithms relatively common at present have Based on amount of calculation vector algorithm, RMS dispatching algorithms, long release time interval priority algorithm, HTSF algorithms etc..In the above method In, static load balancing is all based on based on amount of calculation vector algorithm and RMS dispatching algorithms, the dynamic for not solving message is born Equalization problem is carried, when there are many aperiodicity message in bus, easily causes bus blocking or saturation；Long release time interval Priority algorithm cannot be guaranteed that release is spaced less message or shocking flash and scheduling can be completed before the off period；HTSF algorithms do not have Have and consider that synchronization may have multiple messages while situation about reaching, and algorithm performs are less efficient.
In order to avoid there is the blocking of 1553B buses and saturated phenomenon, the utilization rate of 1553B buses is improved, reduces bus Average delay time, equalizing bus bar load is, it is necessary to design a kind of method of optimization 1553B bus messages transmission.
The content of the invention
(1) technical problems to be solved
The technical problem to be solved in the present invention is：The utilization rate of 1553B buses how is improved, reduces the average retardation of bus Time, equalizing bus bar load, the communication efficiency being optimal.
(2) technical scheme
In order to solve the above-mentioned technical problem, the invention provides a kind of 1553B bus messages based on genetic algorithm Transmission optimization method, comprises the following steps：
S1, the mathematical modeling that 1553B bus messages transmit is established based on queueing theory, obtain object function：
Regard the transmitting procedure of the total Thread Messages of 1553B as a kind of queuing system of single waiter's list queue, be lined up mould Type is a M | M | 1 queuing model, every a piece of news instruction in bus for etc. customer to be serviced, bus passed to provide data Defeated waiter, service time are message transmission time；
The queue discipline of the queuing system is：
The successive arrival time interval of message is independent, it is assumed that arrival time obeys Poisson distribution；
The length of queue queue is endless, and method of service obeys prerequisite variable；
S2,1553B bus messages scheduling feasible solution is obtained based on improved adaptive GA-IAGA：
Chromosome is encoded using genetic algorithm, determines initial population, passes through the genetic manipulation pair in genetic algorithm Initial population optimizes, i.e., according to self-identifying intersection and mutation probability, initial population is intersected, mutation operation, according to The object function is assessed chromosome population, and heredity is terminated when chromosome population evolutionary rate is less than predetermined threshold value and is calculated Method, obtain 1553B bus messages scheduling feasible solution；
S3, based on improve ant group algorithm obtain 1553B bus message scheduling optimum results:
The global search information configuration information element initial value obtained according to genetic algorithm, dispatched according to 1553B bus messages Performance, adjustment information element；When message prepares to transmit, the pheromones of each idling-resource are observed, according to probability size Resource is selected to use；After the message end of transmission, message is evaluated, pheromones feedback is carried out according to information matches degree, obtained To optimal scheduling scheme.
(3) beneficial effect
The present invention first passes through queueing theory and establishes 1553B bus message scheduling mathematic models, and it is quick to be subsequently introduced genetic algorithm 1553B bus messages scheduling feasible solution is found, it is initial that the feasible solution for then finding genetic algorithm is converted into ant colony optimization algorithm Pheromones, finally obtain 1553B bus message scheduling optimum results using the local optimal searching and positive feedback mechanism of ant group algorithm.Emulation Test result indicates that being optimized using the genetic algorithm after improvement to the transmission of 1553B bus messages, can disappear meeting every On the premise of ceasing maximum delay time requirement and Realtime Capability of Communication, bus utilization is improved, effectively alleviates bus message Congestion and saturated phenomenon, solves the balanced problem of bus load, the ability with preferably processing asynchronous message.
Brief description of the drawings
Fig. 1 is M | M | 1 model incidence figure；
Fig. 2 be the embodiment of the present invention method flow in Revised genetic algorithum flow chart；
Fig. 3 be the embodiment of the present invention method flow in improved ant group algorithm flow chart.
Embodiment
To make the purpose of the present invention, content and advantage clearer, with reference to the accompanying drawings and examples, to the present invention's Embodiment is described in further detail.
In order to improve the real-time of 1553B bus messages transmission, the communication delay rate of bus is reduced, this paper presents one kind The 1553B bus message transmission optimization methods of improved adaptive GA-IAGA, comprise the following steps：
1st, the mathematical modeling of 1553B bus messages transmission is established based on queueing theory
The transmitting procedure of the total Thread Messages of 1553B can be regarded as a kind of queuing system of single waiter's list queue.The mould Type is a M | M | 1 queuing model.The model incidence of the queuing system is shown in Fig. 1, and the digitized representation wherein in circle is lined up system The state of system, then understand the birth and death process that the queuing system is a homogeneous markov chain.
In Fig. 1, λ is the average arrival rate of message, and μ is the average service rate of bus, and m is message number, therefore 1/ λ is The average time interval of message, 1/ μ are the average transmission time of message.
If EkFor probability of the queuing system at state k, ρ is bus utilization, establishes the state balance side of birth and death process Journey：
State 0：λρ0=μ E1,E1=ρ E0
State 1：λρ1=μ E2,E2=ρ2E0
……
State k：λρk=μ Ek+1,Ek+1=ρk+1E0 (1)
Assuming that the average time interval of message is more than the average transmission time of message, i.e. ρ ﹤ 1, then following series
Convergence
Thus show that probability of the queuing system under different conditions is respectively：
E0=1- ρ
E1=ρ (1- ρ)
……
Ek=ρk(1-ρ) (3)
……
Bus utilization is
The average of message is in queuing system：
If there is k bar message in queuing system, wherein k-1 bars message is being waited in line, then the message average waited in line For：
Message transmission spend time be：
Message queueing waiting spends the time taking average value to be：
The average delay time of 1553B bus transmission systems is message transmission time and message queueing waiting time sum. It is to make average delay time minimum to the target that aperiodic message transmission optimizes in 1553B buses, and determines to be optimal The optimal bus average service rate μ of desired value*。
It is message transmission time and the desired value of message queue time sum in queuing system to take object function z：
Z=csμ+cwL (9)
Wherein, csFor the bus message transmission spent time as μ=1, cwWait in line in the bus for every message The spent time.WillAbove formula is substituted into, can be obtained
Above formula (10) is the mathematical modeling of 1553B bus messages transmission.
2nd, 1553B bus messages scheduling feasible solution is obtained based on genetic algorithm, with reference to figure 2：
(1) chromosome coding
Message number is set as m, message cycle number is n, and m message will be arranged on n cycle.Individual chromosome On each gene location numbering represent message numbering, integer representation of each gene position between 0~(m-1), represent certain Cycle numbering where message.For example m=6, n=4, chromosome coding are (0,1,2,3,3,2)：Represent the 1st article of message distribution Onto the minor cycle 1, the 2nd article of message was assigned on the minor cycle 2, and the 3rd article and the 6th article of message were assigned on the minor cycle 3, the 4th article and 5th article message was assigned on the minor cycle 4.
(2) initial population is determined
The initialization of population is the key of genetic algorithm, and traditional genetic algorithm determines that initial population takes random life mostly Chromosome scheme is formed into method, so that iteration starts that many infeasible schemes may be formed, largely to be calculated The scheme that can be just optimized afterwards, this largely reduces the operation efficiency of algorithm, and the present invention is to classical genetic algorithm It is improved, the selection algorithm of the initial population after improvement can effectively suppress " precocity " phenomenon, its ability of searching optimum and search Effect is all significantly improved.The producing method of initial population after improvement is as follows：
Individual is randomly generated first, and individual lengths Length, if x and y is two individuals, u is first in population Individual, v are the individuals that similarity-rough set is carried out with u, and the similarity between them is defined as：
Sim (u, v)=1-dist (u, v) (dist (u, v) is Hamming distances function) (11)
Pass through more individual similarity, it is desirable to which regulation can be selected in initial individuals similarity and must be fulfilled for following condition：
Wherein, d represents regulating constant, for controlling desired similarity.
(3) fitness function
Fitness function is used for the quality for evaluating chromosome, and its functional value is bigger to represent that chromosome survival ability is stronger, right The solution answered is optimal.Formula (10) gives the average delay time function of 1553B bus transmission systems, therefore the present invention uses Fitness function is：
I.e.
(4) crossover operation
Conventional interleaved mode have some intersections, two-point crossover, multiple-spot detection with it is consistent intersection etc..The present invention is calculated heredity Method is improved, by the way of multi-point single-gene intersection, with parent optimal solution TmaxCarry out intersecting behaviour with child chromosome pond Make, this method can avoid algorithm from prematurely losing evolvability.Comprise the following steps that：
A) selection carries out the chromosome T of crossover operation in the T of chromosome pondiWith optimal chromosome Tmax；
B) random generation intersects fragment and intersection region；
C) by TiIntersection region be added to TmaxAbove, by TmaxIntersection region be added to TiAbove；
D) deletion and TiOr TmaxIntersection region identical gene, obtain two new filial generations.
(5) mutation operation
Aberration rate generally takes 0.1 in mutation operator, in individual chromosome Ti=(ti1,ti2,…,tim) on randomly choose connect Continue multiple genes, multiple genes are carried out to rearrange the variation for realizing chromosome.As a result of optimum individual TmaxRetain Strategy, so in mutation operator, the probability scanned near current optimum individual can be increased, without worrying to destroy Through existing excellent chromosome.
(6) operation is replicated
Chromosome is evaluated based on fitness function fitness (f (x)), fitness is higher than predetermined threshold value Chromosome be copied directly in chromosome of future generation.
(7) selection operation
By aforesaid operations, the chromosome population of a new generation is obtained.Obtained chromosome population is entered based on object function z Row is assessed, if being unsatisfied with to current scheduling scheme, repeat the above steps the genetic manipulation process of (1) to (6)；When chromosome kind Group terminates genetic algorithm when evolutionary rate is less than predetermined threshold value.
3rd, 1553B bus message scheduling optimum results are obtained based on ant group algorithm, with reference to figure 3:
Set bi(t) represent that moment t is located at period piAnt number, then haveUse τi(t) t is represented In i-th of period piPheromones value, initial time τi(0)=ri-loadi(0), wherein i=1,2 ..., n, riFor i-th week The processing message capability that phase is possessed, loadi(0) obtained current optimal scheduling scheme is assigned to the when being terminated for genetic algorithm I period piMessage occupied by bus load.
Improved ant group algorithm is described as follows：
A) m ant was respectively placed in the corresponding cycle, and initial value, τ is assigned for the pheromones in each cyclei(0)=ri- loadi(0), wherein i=1,2 ..., n；
B) ant is placed on oriented node of graph；
If c) there is message from i-th of period piTransmission success, then assign pheromones increment Delta τ for the cyclei=Ce×K；It is no Then, pheromones increment Delta τ is assigned for the cyclei=Cp×K.Wherein K represents the time overhead used in transmission corresponding message, CeWith CpRepresent the corresponding rewards and punishments factor；
D) the rhythmic pheromones of renewal institute, i.e. τi(t)=τi(t)+Δτi, wherein i=1,2 ..., n；
E) according to the pheromones distribution situation in each cycle, probability is calculated：
Wherein：τi(t) it is i-th of period p of tiPheromone concentration；ηiFor i-th of period piThe processing possessed disappears Breath ability；α represents the importance of i-th of cycle information element, and β represents the processing message energy that the pheromones in i-th of cycle are possessed The significance level of power,Represent that message m takes i-th of period p in tiProbability；
It is that every ant chooses next cycle p respectively based on obtained most probable valuei；
F) cycle chosen according to all ants, fitness function fitness (f (x)) corresponding to calculating, fitness (f (x)) value is bigger, and corresponding scheduling scheme is better, the current optimal scheduling scheme of record；
If g) reach maximum iterations, or there is degradation phenomena in iteration, then current record scheduling optimum result is The optimal scheduling scheme of gained；Otherwise, the ant collection of all ants is emptied, jumps to step c).
Ant group algorithm after improvement is compared with the ant group algorithm before improvement, introduced on step c) and step d) rewards and punishments because Sub- CeAnd Cp, avoid and the short message of transmission time is arranged on larger transmission cycle, what logical adjustment each cycle was selected Probability, it is more reasonably each message assignment period, is advantageous to improve bus message efficiency of transmission.
Described above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art For member, without departing from the technical principles of the invention, some improvement and deformation can also be made, these are improved and deformation Also it should be regarded as protection scope of the present invention.

Claims (1)

  1. A kind of 1. 1553B bus message transmission optimization methods based on genetic algorithm, it is characterised in that including following step Suddenly：
    S1, the mathematical modeling that 1553B bus messages transmit is established based on queueing theory, obtain object function：
    Regard the transmitting procedure of the total Thread Messages of 1553B as a kind of queuing system of single waiter's list queue, queuing model is One M | M | 1 queuing model, every a piece of news instruction in bus for etc. customer to be serviced, bus is provides data transfer Waiter, service time are message transmission time；
    The queue discipline of the queuing system is：
    The successive arrival time interval of message is independent, it is assumed that arrival time obeys Poisson distribution；
    The length of queue queue is endless, and method of service obeys prerequisite variable；
    S2,1553B bus messages scheduling feasible solution is obtained based on improved adaptive GA-IAGA：
    Chromosome is encoded using genetic algorithm, determines initial population, by the genetic manipulation in genetic algorithm to initial Population optimizes, i.e., according to self-identifying intersection and mutation probability, initial population is intersected, mutation operation, according to described Object function is assessed chromosome population, and genetic algorithm is terminated when chromosome population evolutionary rate is less than predetermined threshold value, Obtain 1553B bus messages scheduling feasible solution；
    S3, based on improve ant group algorithm obtain 1553B bus message scheduling optimum results:
    The global search information configuration information element initial value obtained according to genetic algorithm, dispatched according to 1553B bus messages complete Into situation, adjustment information element；When message prepares to transmit, the pheromones of each idling-resource are observed, are selected according to probability size Resource uses；After the message end of transmission, message is evaluated, pheromones feedback is carried out according to information matches degree, obtained most Excellent scheduling scheme；
    The object function that step S1 is obtained is：Wherein λ is the average arrival rate of message, and μ is bus Average service rate, csFor the bus message transmission spent time as μ=1, cwWait in line institute in the bus for every message The time of consuming；
    Step S2 is specifically included：
    S21, carry out chromosome coding：
    Message number is set as m, message cycle number is n, and m message will be arranged on n cycle, in individual chromosome Each gene location numbering represents message numbering, integer representation of each gene position between 0~(m-1), represents certain message The cycle numbering at place；
    S22, determine initial population：
    Individual is randomly generated first, and individual lengths Length, u are the individuals in population, and v is similar to u progress The individual compared is spent, the similarity between them is defined as：Sim (u, v)=1-dist (u, v), wherein dist (u, v) are hamming Distance function
    Pass through more individual similarity, it is desirable to which regulation can be selected in initial individuals similarity and meet following condition：
    <mrow> <mi>s</mi> <mi>i</mi> <mi>m</mi> <mrow> <mo>(</mo> <mi>u</mi> <mo>,</mo> <mi>v</mi> <mo>)</mo> </mrow> <mo>&lt;</mo> <mfrac> <mrow> <mi>L</mi> <mi>e</mi> <mi>n</mi> <mi>g</mi> <mi>t</mi> <mi>h</mi> <mo>-</mo> <mi>d</mi> </mrow> <mrow> <mi>L</mi> <mi>e</mi> <mi>n</mi> <mi>g</mi> <mi>t</mi> <mi>h</mi> </mrow> </mfrac> <mo>,</mo> <mi>u</mi> <mo>&amp;NotEqual;</mo> <mi>v</mi> </mrow>
    Wherein d represents regulating constant, for controlling desired similarity；
    S23, determine fitness function
    The fitness function is used for the quality for evaluating chromosome, and functional value is bigger to represent that chromosome survival ability is stronger, corresponding Xie Yueyou, obtaining fitness function is：
    <mrow> <mi>f</mi> <mi>i</mi> <mi>t</mi> <mi>n</mi> <mi>e</mi> <mi>s</mi> <mi>s</mi> <mrow> <mo>(</mo> <mi>f</mi> <mo>(</mo> <mi>x</mi> <mo>)</mo> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <msub> <mi>c</mi> <mi>s</mi> </msub> <mi>&amp;mu;</mi> <mo>+</mo> <msub> <mi>c</mi> <mi>w</mi> </msub> <mo>&amp;CenterDot;</mo> <mfrac> <mi>&amp;lambda;</mi> <mrow> <mi>&amp;mu;</mi> <mo>-</mo> <mi>&amp;lambda;</mi> </mrow> </mfrac> </mrow> </mfrac> </mrow>
    S24, perform crossover operation
    By the way of multi-point single-gene intersection, with parent optimal solution TmaxCrossover operation is carried out with child chromosome pond；
    S25, perform mutation operation
    In individual chromosome Ti=(ti1,ti2,…,tim) on the continuous multiple genes of random selection, multiple genes are arranged again Row realize the variation of chromosome, i=1,2 ..., n；
    S26, perform and replicate operation
    Chromosome is evaluated based on fitness function fitness (f (x)), fitness is higher than to the dye of predetermined threshold value Colour solid is copied directly in chromosome of future generation；
    S27, perform selection operation
    By aforesaid operations, the chromosome population of a new generation is obtained, obtained chromosome population is commented based on object function z Estimate, if being unsatisfied with to current scheduling scheme, the genetic manipulation process for the S21 to S26 that repeats the above steps；When chromosome population enters Change when speed is less than predetermined threshold value and terminate genetic algorithm；
    Step S24 is specially：
    A) selection carries out the chromosome T of crossover operation in the T of chromosome pondiWith optimal chromosome Tmax；
    B) random generation intersects fragment and intersection region；
    C) by TiIntersection region be added to TmaxAbove, by TmaxIntersection region be added to TiAbove；
    D) deletion and TiOr TmaxIntersection region identical gene, obtain two new filial generations；
    Step S3 is specially：
    Set bi(t) represent that moment t is located at period piAnt number, then haveUse τi(t) represent t i-th Individual period piPheromones value；
    A) m ant was respectively placed in the corresponding cycle, and initial value, τ are assigned for the pheromones in each cyclei(0)=ri-loadi (0), wherein i=1,2 ..., n, riThe processing message capability possessed by i-th of cycle, loadi(0) when being terminated for genetic algorithm Obtained current optimal scheduling scheme is assigned to i-th of period piMessage occupied by bus load；
    B) ant is placed on oriented node of graph；
    C) if having message from i-th of period piTransmission success, then assign pheromones increment Delta τ for the cyclei=Ce×K；Otherwise, Pheromones increment Delta τ is assigned for the cyclei=Cp× K, wherein K represent the time overhead used in transmission corresponding message, CeAnd CpTable Show the corresponding rewards and punishments factor；
    D the rhythmic pheromones of) renewal institute, i.e. τi(t)=τi(t)+Δτi, wherein i=1,2 ..., n；
    E probability) is calculated according to the pheromones distribution situation in each cycle：
    <mrow> <msubsup> <mi>p</mi> <mi>i</mi> <mi>m</mi> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <msup> <mrow> <mo>&amp;lsqb;</mo> <msub> <mi>&amp;tau;</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> <mi>&amp;alpha;</mi> </msup> <mo>&amp;times;</mo> <msup> <mrow> <mo>&amp;lsqb;</mo> <msub> <mi>&amp;eta;</mi> <mi>i</mi> </msub> <mo>&amp;rsqb;</mo> </mrow> <mi>&amp;beta;</mi> </msup> </mrow> <mrow> <msub> <mi>&amp;Sigma;</mi> <mi>i</mi> </msub> <msup> <mrow> <mo>&amp;lsqb;</mo> <msub> <mi>&amp;tau;</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> <mi>&amp;alpha;</mi> </msup> <mo>&amp;times;</mo> <msup> <mrow> <mo>&amp;lsqb;</mo> <msub> <mi>&amp;eta;</mi> <mi>i</mi> </msub> <mo>&amp;rsqb;</mo> </mrow> <mi>&amp;beta;</mi> </msup> </mrow> </mfrac> </mrow>
    Wherein：τi(t) it is i-th of period p of tiPheromone concentration；ηiFor i-th of period piThe processing message energy possessed Power；α represents the importance of i-th of cycle information element, and β represents the processing message capability that the pheromones in i-th of cycle are possessed Significance level,Represent that message m takes i-th of period p in tiProbability；
    It is that every ant chooses next cycle p respectively based on obtained most probable valuei；
    F) the cycle chosen according to all ants, fitness function fitness (f (x)) corresponding to calculating, fitness (f (x)) Value is bigger, and corresponding scheduling scheme is better, the current optimal scheduling scheme of record；
    If reach maximum iterations, or there is degradation phenomena in iteration, then current record scheduling optimum result is gained Optimal scheduling scheme；Otherwise, the ant collection of all ants is emptied, jumps to step C).
CN201510758135.9A 2015-11-06 2015-11-06 1553B bus message transmission optimization methods based on genetic algorithm Active CN105205536B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201510758135.9A CN105205536B (en) 2015-11-06 2015-11-06 1553B bus message transmission optimization methods based on genetic algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201510758135.9A CN105205536B (en) 2015-11-06 2015-11-06 1553B bus message transmission optimization methods based on genetic algorithm

Publications (2)

Publication Number Publication Date
CN105205536A CN105205536A (en) 2015-12-30
CN105205536B true CN105205536B (en) 2017-11-10

Family

ID=54953205

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201510758135.9A Active CN105205536B (en) 2015-11-06 2015-11-06 1553B bus message transmission optimization methods based on genetic algorithm

Country Status (1)

Country Link
CN (1) CN105205536B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107370689B (en) * 2017-09-08 2020-03-31 天津津航计算技术研究所 Message load balancing method of 1553B bus
CN112114981B (en) * 2020-09-09 2023-12-12 天津津航计算技术研究所 1553B bus message scheduling method based on linked list
CN112477846B (en) * 2020-11-13 2021-12-10 东南大学 Intelligent networking electric automobile queue control method giving consideration to stability and energy conservation
CN114650195B (en) * 2022-05-24 2022-07-29 深圳市德兰明海科技有限公司 Method for calculating frame collision rate of frames on CAN bus and method for minimizing CAN bus resources

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103414624A (en) * 2013-07-29 2013-11-27 北京汇能精电科技有限公司 Network scheduling algorithm of CAN bus master-slave answer mode protocol

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10211517A1 (en) * 2001-03-15 2002-09-26 Bosch Gmbh Robert Bus schedule formation method for transmission of information along bus system uses generic algorithm

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103414624A (en) * 2013-07-29 2013-11-27 北京汇能精电科技有限公司 Network scheduling algorithm of CAN bus master-slave answer mode protocol
CN103634184A (en) * 2013-07-29 2014-03-12 北京汇能精电科技有限公司 Network scheduling algorithm for CAN (controller area network) bus master-slave answer mode protocol

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
1553B总线上消息的实时调度;赵昶宇等;《光学精密工程》;20100331;第18卷(第3期);正文第3节 *
基于遗传算法的制造网络服务质量优化;郭于明等;《华南理工大学学报（自然科学版）》;20070831;第35卷(第8期);摘要，正文第2节，附图5 *
遗传算法在蚁群算法中的融合研究;肖宏峰等;《小型微型计算机系统》;20090331;第30卷(第3期);摘要，正文第3.2节 *

Also Published As

Publication number Publication date
CN105205536A (en) 2015-12-30

Similar Documents

Publication Publication Date Title
CN105205536B (en) 1553B bus message transmission optimization methods based on genetic algorithm
Kurdi An effective new island model genetic algorithm for job shop scheduling problem
CN111325356A (en) Neural network search distributed training system and training method based on evolutionary computation
Kurdi A new hybrid island model genetic algorithm for job shop scheduling problem
CN103489042B (en) A kind of method of calamity emergency decision system path optimization
CN102063339B (en) Resource load balancing method and equipment based on cloud computing system
CN107317699B (en) Dynamic ant colony rapid optimization method of cloud manufacturing service combination
CN106484512B (en) The dispatching method of computing unit
CN103345657A (en) Task scheduling method based on heredity and ant colony in cloud computing environment
CN108874525A (en) A kind of service request distribution method towards edge calculations environment
CN103530709A (en) Container quay berth and quay crane distribution method based on bacterial foraging optimization method
CN109522104B (en) Method for optimizing scheduling of two target tasks of Iaas by using differential evolution algorithm
CN113098714A (en) Low-delay network slicing method based on deep reinforcement learning
CN110389819A (en) A kind of dispatching method and system of computation-intensive batch processing task
CN105550033A (en) Genetic-tabu hybrid algorithm based resource scheduling policy method in private cloud environment
CN104317646B (en) Based on cloud data center dispatching method of virtual machine under OpenFlow frameworks
CN106611378A (en) Priority encoding-based hybrid genetic algorithm for solving job-shop scheduling problem
Ye et al. A new approach for resource scheduling with deep reinforcement learning
Wei et al. Research on cloud design resources scheduling based on genetic algorithm
Liao et al. An ant colony optimization algorithm for setup coordination in a two-stage production system
CN104506576B (en) A kind of wireless sensor network and its node tasks moving method
CN110888728B (en) Task scheduling method of button cluster server
CN115374853A (en) Asynchronous federal learning method and system based on T-Step polymerization algorithm
CN108537338A (en) Disaster assistance emergency resources dispatching method based on multi-Agent Genetic Algorithm
CN104199820A (en) Cloud platform MapReduce workflow scheduling optimizing method

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant