memetic algorithm

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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

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