Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

memetic algorithm


Published on

  • Be the first to comment

  • Be the first to like this

memetic algorithm

  1. 1. Ehsan Ramezani MohammadAmin Amjadi Shahid bahonar university May 2013 11/19/2015
  2. 2.  Energy conservation probably constitutes the most important challenge in the design of wireless sensor networks (WSNs).  Another issue in WSN design is the connectivity of the network according to some specific communication protocol (Cluster-based architecture). 11/19/2015
  3. 3.  The purpose of the sensor network, which is the collection and possibly the management of measured data for some particular application, must not be neglected.  Most algorithms that lead to optimal topologies of WSNs towards power conservation, do not take into account the principles , characteristics and requirements of application-specific WSNs. 11/19/2015
  4. 4.  One of the most powerful heuristics that could be applied to our multi-objective optimization problem is based on “Genetic Algorithms” (GAs).  an integrated GA approach , both in the direction of degrees of freedom of network characteristics and of application-specific requirements represented in the performance metric of the GA is considered. 11/19/2015
  5. 5.  optimization problem → minimization of the energy-related parameters and the maximization of sensing points’ uniformity, subject to the connectivity constraints and the spatial density requirement.  ↓  single objective function  ↓  minimization 11/19/2015
  6. 6.  he original GA-based algorithm was applied dynamically to obtain a dynamic sequence of operating modes for each sensor.  i.e. a sequence of WSN designs, which leads to maximization of network lifetime in terms of number of data collection (measuring) cycles. 11/19/2015
  7. 7.  Memetic algorithms introduce local search techniques at specific parts of a GA optimization process, with a goal to improve its performance.  In this work, we develop and parameterize a memetic algorithm (MA) which hybridizes the GA system and the goal being to improve its performance by guiding the population formulation of the GA towards more intelligent decisions. 11/19/2015
  8. 8.  WSN modeling  Design parameters 11/19/2015
  9. 9.  A cluster-based network architecture is used where sensors are partitioned into several clusters.  All sensors are identical and may be either active or inactive.  Sensores three supported signal ranges:  1)CH sensor that allows the communication with the remote base station (sink).  2) HSR sensor  3)LSR sensor 11/19/2015
  10. 10.  An evolutionary algorithm, requires the proper definition of some optimization criteria.  In order for the optimization to be possible, it is required that these design parameters are explicitly defined and expressed mathematically.  Application specific parameters  Connectivity parameters  Energy related parameters 11/19/2015
  11. 11.  he satisfaction or not of the demand on uniformity of measuring points has been taken into consideration using two design parameters:  (a) MRD : the spatial mean relative deviation of sensing points, representing the uniformity of those points .  (b) the desired spatial density of measuring points. 11/19/2015
  12. 12.  MRD →minimized ≡ uniformity is maximized  The spatial density of sensing points has to be as close as possible to the desired value.  The entire area of interest was divided into several overlapping sub-areas.  Sub-areas are defined by four factors : length and width(size) , overlapping ratio (ratios in the two directions). 11/19/2015
  13. 13.  MRD= 𝑖=1 𝑁 |𝑃𝑠𝑖 − 𝑃𝑠| 𝑁 . 𝑃𝑠  Ps : spatial density of sensing points in total area  Psi : spatial density of sensing points in sub-area Si  N : number of overlapping sub-areas into which the entire area was divided  Low MRD → high uniformity  11/19/2015
  14. 14.  SDE = 𝑃𝑑 −𝑃𝑠 𝑃𝑑 𝑖𝑓 𝑃𝑠 < 𝑃𝑑 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒  Pd : set to 0.2 11/19/2015
  15. 15.  This set of design parameters includes two factors : first : that each clusterhead does not have more than a maximum predefined number of sensors in its cluster (SCE) second : that each sensor of the network can communicate with its clusterhead (SORE) 11/19/2015
  16. 16.  SCE = 𝑖=1 𝑛 𝑓𝑢𝑙𝑙 𝑛𝑖 𝑛 𝑓𝑢𝑙𝑙 𝑖𝑓 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 𝑛 𝑓𝑢𝑙𝑙 > 0  𝑛 𝑓𝑢𝑙𝑙 : number of clusterheads that have more than 15 active sensors in clusters  𝑛𝑖 : number of sensors in the ith of those clusters 11/19/2015
  17. 17.  SORE = 𝑛 𝑜𝑢𝑡 𝑛  𝑛 𝑜𝑢𝑡 : number of active sensors that cannot communicate with their clusterhead  n : total number of active sensors in the network 11/19/2015
  18. 18.  This set of design parameters can be divided into two sub-categories : OE : operational energy consumption parameter CE: communicational energy consumption parameter 11/19/2015
  19. 19.  OE = 20 . 𝑛 𝐶𝐻 𝑛 + 2 . 𝑛 𝐻𝑆 𝑛 + 𝑛 𝐿𝑆 𝑛  𝑛 𝐶𝐻 : operation mode of the sensor is CH  𝑛 𝐻𝑆 : operation mode of the sensor is HS  𝑛 𝐿𝑆 : operation mode of the sensor is LS  corresponding relevance factors for three active operating modes of the sensors : 20:2:1 11/19/2015
  20. 20.  CE = 𝑖=1 𝑐 𝑗=1 𝑛 𝑖 𝜇. 𝑑𝑗𝑖 𝑘  c : number of clusters in the network  𝑛𝑖 : number of sensors in the ith cluster  𝑑𝑗𝑖:Euclidean distance from sensor j to its clusterhead  𝜇 & k : constants 11/19/2015
  21. 21.  The maximization of the life duration of the network depends mainly on the remaining battery capacities of the sensors.  𝐵𝐶𝑃 𝑡 = 𝑖=1 𝑛 𝑔𝑟𝑖𝑑 𝑃𝐹𝑖 𝑡 . 1 𝐵 𝐶𝑡 𝑡 − 1 ,t=1,2,…  𝐵𝐶𝑖 𝑡 = 𝐵𝐶𝑖 𝑡−1 − 𝐵𝑅𝑅𝑖 𝑡−1 11/19/2015
  22. 22.  𝐵𝐶𝑃 𝑡 : Battery Capacity Penalty of the WSN at measuring cycle t  𝑛 𝑔𝑟𝑖𝑑 : total number of available sensor nodes  𝑃𝐹𝑖 𝑡 : Penalty Factor assigned to sensor i  𝐵𝐶𝑖 𝑡 and 𝐵𝐶𝑖 𝑡−1 : are the Battery Capacities of sensor i at measuring cycles t and t – 1  𝐵𝑅𝑅𝑖 𝑡−1 : Battery Reduction Rate 11/19/2015
  23. 23.  Methodology of GA:  i) encoding mechanism of the problems phenotypes into genotypes ii) formulation of an appropriate fitness function iii) choice of the genetic operators and the selection mechanism 11/19/2015
  24. 24.  The parameters of each WSN design that needs to be encoded in the representation scheme of the GA are the following:  i) placement of the active sensors of the network  ii) operation mode of each active sensor(clusterhead or a ‘‘regular sensor”)  iii) range of signal (high or low),in ‘‘regular sensor”  X sensors in the WSN → each string in the GA population has a length of 2X 11/19/2015
  25. 25.  F= 1 (𝛼1 . 𝑀𝑅𝐷 + 𝛼2. 𝑆𝐷𝐸 + 𝛼3. 𝑆𝐶𝐸 + 𝛼4. 𝑆𝑂𝑅𝐸 + 𝛼5. 𝑂𝐸 + 𝛼6. 𝐶𝐸 + 𝛼7. 𝐵𝐶𝑃)  𝛼i : weighting coefficients  The values of these coefficients were determined based on experience about the importance of each parameter. 11/19/2015
  26. 26.  The concept of the memetic algorithm is based on the introduction of some battery level threshold values for each operating mode of the sensors.  at each measuring cycle to allow a sensor i to operate at some specific mode if and only if its battery level at the time is above the threshold value for that operating mode. 11/19/2015
  27. 27.  The MA approach is materialized through two separate processes:  (i) the local search : battery level of each sensor against the corresponding threshold value  (ii) the appropriate update of the threshold values for each operating mode, according to some specific reduction scheme.  11/19/2015
  28. 28.  The main part of the MA is the local search that is performed in the generation of the population of the original GA.  local search is performed in each individual of the GA population . 11/19/2015
  29. 29.  Some initial threshold values of battery levels for each of the three possible operating modes of the sensors are defined.  he intention of these threshold values is to put specific constraints in the operation modes of each sensor, throughout the dynamic design of the network.  Operating mode of each sensor is checked 11/19/2015
  30. 30.  battery level is below threshold ↓  operating mode is changed to the lower mode ↓  until its corresponding threshold value becomes lower than (or equal to) its battery level 11/19/2015
  31. 31.  Three facts are evident about the proposed modification:  First, it is applied to the phenotype of the design problem and not the genotype of the optimization process of the GA  Second, investigation on the appropriate level of reduction of the operating mode of the modified sensors resembles some kind of search operation.  Third, modification always leads towards the direction of a local improvement , without any assurance that this would result into a generally better solution. 11/19/2015
  32. 32.  Threshold values of battery levels for each of the three possible operating modes of the sensors are initialized.  Three parameters of reduction scheme of each threshold: initial value reduction formula reduction rate 11/19/2015
  33. 33.  (i) geometric reduction: 𝑇 𝑡+1 = (1 − 𝑅𝑅). 𝑇 𝑡 T : any of the three types of threshold t : some specific measuring cycle RR : reduction rate parameter  1t T  11/19/2015
  34. 34.  (ii) linear reduction: 𝑇 𝑡+1 = 𝑇 𝑡 − 𝑅𝑅 (iii) no update, where thresholds are kept constant over time. 11/19/2015
  35. 35.  he performance of the MA approach to the dynamic optimization of WSN designs was compared to that of the original GA-based system, during 15 consecutive measuring cycles of the WSN. 11/19/2015
  36. 36. 11/19/2015
  37. 37. 11/19/2015
  38. 38.  The MA system showed considerable improvement in energy conservation of the network resources over the already successful performance of the GA system , while the application-specific characteristics of the sensor networks were kept close to optimal values. 11/19/2015
  39. 39.  The satisfactory performance of the algorithm during the dynamic network design process makes it a valuable tool for design optimization towards maximization of the life span of WSNs, especially in cases where satisfaction of some application-specific requirements is a necessity. 11/19/2015
  40. 40.  In addition, it was shown that appropriate manipulation of the population of the GA (something that was introduced by the proposed local search scheme) can lead to performance improvement. 11/19/2015
  41. 41.  Thanks for listening!!! 11/19/2015