TopologieObjectiveFunction.java 10 KB

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285
  1. package algorithm.objectiveFunction;
  2. import ui.model.DecoratedHolonObject;
  3. import ui.model.DecoratedNetwork;
  4. import ui.model.DecoratedState;
  5. import ui.model.DecoratedSwitch;
  6. import java.util.HashSet;
  7. import java.util.Locale;
  8. import algorithm.objectiveFunction.GraphMetrics.Graph;
  9. import api.TopologieAlgorithmFramework.IndexCable;
  10. public class TopologieObjectiveFunction {
  11. //Parameters
  12. //weight for f_g(H)
  13. static double w_eb = 0.3, w_max = 0.2, w_holon= 0.1, w_selection = .3, w_grid = 0.1;
  14. //--> f_eb parameter
  15. /**
  16. * Maximum Energie Difference(kappa)
  17. */
  18. static double k_eb = 20000.f;
  19. /**
  20. * Maximum when all on Energie Difference(kappa)
  21. */
  22. static double k_max = 10.f;
  23. static double lambda_max = 10.;
  24. //--> f_holon parameter
  25. /**
  26. * maximum penalty from holon element distribution
  27. */
  28. static double k_holon= 100000;
  29. //--> f_selection paramaeter;
  30. /**
  31. * average Maximum Cost for selction(kappa) of switch and elements.
  32. */
  33. static double k_selection = 50000;
  34. static double cost_switch = 20;
  35. private static double cost_of_cable_per_meter = 0.8;
  36. //--> f_grid parameter
  37. /**
  38. * The avergae shortest path maximum length -> kappa for the squash function
  39. */
  40. static double k_avg_shortest_path = 1600;
  41. //Disjpijoint path cant have zero as output it starts with the value 1
  42. static double centerValue_disjoint_path = 1.0;
  43. static double k_disjoint_path = 2.4;
  44. static double lambda_avg_shortest_path = 10;
  45. static double lambda_disjoint_path = 10;
  46. static double k_grid = lambda_avg_shortest_path;// + lambda_disjoint_path;
  47. //pre-calculated parameters for partial function terms:
  48. /**
  49. * Pre calculated for the squash function
  50. * <br>
  51. * {@link TopologieObjectiveFunction#squash}
  52. */
  53. static double squash_subtract = 1.0f / (1.f + (float) Math.exp(5.0));
  54. static double range_for_k_avg_shortest_path = range(k_avg_shortest_path);
  55. static double range_for_k_disjoint_path = range(k_disjoint_path - centerValue_disjoint_path);
  56. static {
  57. //init
  58. checkParameter();
  59. }
  60. /**
  61. * Check parameter Setting and print error when wrong values are put in.
  62. * Here should all invariants be placed to be checked on initialization.
  63. */
  64. private static void checkParameter() {
  65. if(!(Math.abs(w_eb + w_holon + w_selection + w_grid + w_max - 1) < 0.001)) {
  66. System.err.println("ParameterError in ObjectiveFunction: Sum of all weights should be 1");
  67. }
  68. }
  69. /**
  70. * ObjectifeFunction by Carlos.
  71. * Function computes f_g:
  72. * f_g = w1 * squash(f_eb, k1) + w2 * squash(f_state, k2) + w3 * squash(f_pro, k3) + w4 * squash(f_perf, k4) + w5 * squash(f_holon, k5)
  73. *
  74. *
  75. * squash is the squashing function {@link TopologieObjectiveFunction#squash}
  76. *
  77. *
  78. * @param state
  79. * @param moreInformation TODO
  80. * @return f_g value between 0 and 100
  81. */
  82. static public float getFitnessValueForState(DecoratedState state, int amountOfAddedSwitch, double addedCableMeters, boolean moreInformation) {
  83. //Calculate f_eb the penalty for unbalenced energy in the network
  84. double f_eb = 0;
  85. for(DecoratedNetwork net : state.getNetworkList()) {
  86. //abs
  87. f_eb += Math.abs(net.getTotalConsumption() - net.getTotalProduction());
  88. }
  89. //Average?
  90. f_eb /= state.getNetworkList().size();
  91. double f_maximum = 0;
  92. for(DecoratedNetwork net : state.getNetworkList()) {
  93. double prod = net.getTotalProduction();
  94. double con = net.getTotalConsumption();
  95. if(prod == 0 || con == 0) {
  96. f_maximum += lambda_max;
  97. }else {
  98. f_maximum += lambda_max * (Math.abs(prod - con)/Math.max(prod, con));
  99. }
  100. }
  101. //Average?
  102. f_maximum /= state.getNetworkList().size();
  103. //calculate f_holon
  104. double f_holon = 0;
  105. for(DecoratedNetwork net : state.getNetworkList()) {
  106. double f_elements_deviation_production = net.getDeviationInProductionInNetworkForHolonObjects();
  107. double f_elements_deviation_consumption = net.getDeviationInConsumptionInNetworkForHolonObjects();
  108. double f_element = f_elements_deviation_production+f_elements_deviation_consumption;
  109. f_holon += f_element;
  110. }
  111. //calculating f_selection
  112. double f_selection = 0;
  113. double cost = 0;
  114. for(DecoratedNetwork net : state.getNetworkList()) {
  115. for(DecoratedHolonObject dHobject : net.getConsumerList()) {
  116. if(dHobject.getModel().getName().contains("Wildcard")){
  117. if(dHobject.getModel().getName().length() > 9) {
  118. String costString = dHobject.getModel().getName().substring(9);
  119. cost += Double.parseDouble(costString);
  120. }
  121. }
  122. }
  123. for(DecoratedHolonObject dHobject : net.getConsumerSelfSuppliedList()) {
  124. if(dHobject.getModel().getName().contains("Wildcard")){
  125. if(dHobject.getModel().getName().length() > 9) {
  126. String costString = dHobject.getModel().getName().substring(9);
  127. cost += Double.parseDouble(costString);
  128. }
  129. }
  130. }
  131. for(DecoratedHolonObject dHobject : net.getSupplierList()) {
  132. if(dHobject.getModel().getName().contains("Wildcard")){
  133. if(dHobject.getModel().getName().length() > 9) {
  134. String costString = dHobject.getModel().getName().substring(9);
  135. cost += Double.parseDouble(costString);
  136. }
  137. }
  138. }
  139. }
  140. f_selection += cost;
  141. f_selection += cost_switch * amountOfAddedSwitch;
  142. f_selection += cost_of_cable_per_meter * addedCableMeters;
  143. //if(moreInformation)System.out.println("CostForWildcards:" + cost + ", CostSwitches(#" + amountOfAddedSwitch +"):" + cost_switch * amountOfAddedSwitch + ", CostCables(" +addedCableMeters+ "m):" + cost_of_cable_per_meter * addedCableMeters);
  144. //calculating f_grid
  145. double f_grid = 0;
  146. //each network is a holon
  147. for(DecoratedNetwork net: state.getNetworkList()) {
  148. Graph G = GraphMetrics.convertDecoratedNetworkToGraph(net);
  149. //We have to penalize single Networks;
  150. if(G.V.length <= 1 || G.S.length <= 1) {
  151. f_grid += lambda_avg_shortest_path;// + lambda_disjoint_path;
  152. continue;
  153. }
  154. double avgShortestPath = GraphMetrics.averageShortestDistance(G);
  155. //double disjpointPaths = GraphMetrics.averageEdgeDisjointPathProblem(G);
  156. f_grid += avgShortestPathPenalty(avgShortestPath);// + disjoinPathPenalty(disjpointPaths);
  157. }
  158. //take average to encourage splitting
  159. f_grid /= state.getNetworkList().size();
  160. if(moreInformation) {
  161. printWeightedValues(f_eb, f_maximum, f_holon, f_selection, f_grid);
  162. }
  163. //printUnsquashedValues(f_eb, f_maximum, f_holon, f_selection, f_grid);
  164. return (float) (w_eb * squash(f_eb, k_eb)
  165. + w_max * squash(f_maximum, k_max)
  166. + w_holon * squash(f_holon, k_holon)
  167. + w_selection * squash(f_selection, k_selection)
  168. + w_grid * squash(f_grid, k_grid));
  169. }
  170. private static String doubleToString(double value) {
  171. return String.format (Locale.US, "%.2f", value);
  172. }
  173. private static double disjoinPathPenalty(double value) {
  174. return -(2.0 * lambda_disjoint_path) / (1 + Math.exp(- (value - centerValue_disjoint_path)/ range_for_k_disjoint_path)) + (2.0 * lambda_disjoint_path);
  175. }
  176. private static double avgShortestPathPenalty(double value) {
  177. return (2.0 * lambda_avg_shortest_path) / (1 + Math.exp(- value/ range_for_k_avg_shortest_path)) - lambda_avg_shortest_path;
  178. }
  179. /**
  180. * Attention Math.log calcultae ln not log
  181. * @param kappa
  182. * @return
  183. */
  184. private static double range(double kappa) {
  185. return - kappa / Math.log(Math.pow(2.0, 0.05) - 1.0 );
  186. }
  187. /**
  188. * The squashing function in paper
  189. * @param x the input
  190. * @param kappa the corresponding kappa
  191. * @return
  192. */
  193. static public double squash(double x, double kappa) {
  194. return 100.f/(1.0f + Math.exp(-(10.f * (x - kappa/2.f))/ kappa)) - squash_subtract;
  195. }
  196. /**
  197. * f_sup in paper
  198. * @param supply from 0 to 1
  199. * @return
  200. */
  201. static public double supplyPenalty(double supply) {
  202. double supplyPercentage = 100 * supply;
  203. return (supplyPercentage < 100) ? -0.5 * supplyPercentage + 50: supplyPercentage - 100;
  204. }
  205. static void printWeightedValues(double f_eb, double f_maximum, double f_holon, double f_selection, double f_grid){
  206. System.out.println("===================================================================");
  207. System.out.println(" f_eb: " + f_eb + ", k_eb: " + k_eb + ", w_eb: " + w_eb);
  208. System.out.println(" squash(f_eb, k_eb): " + doubleToString(squash(f_eb, k_eb)));
  209. System.out.println(" w_eb * squash(f_eb, k_eb): " + doubleToString(w_eb * squash(f_eb, k_eb)));
  210. System.out.println("===================================================================");
  211. System.out.println(" f_maximum: " + f_maximum + ", k_max: " + k_max + ", w_max: " + w_max);
  212. System.out.println(" squash(f_maximum, k_max): " + doubleToString(squash(f_maximum, k_max)));
  213. System.out.println(" w_max * squash(f_maximum, k_max): " + doubleToString(w_max * squash(f_maximum, k_max)));
  214. System.out.println("===================================================================");
  215. System.out.println(" f_selection: " + f_selection + ", k_selection: " + k_selection + ", w_selection: " + w_selection);
  216. System.out.println(" squash(f_selection, k_selection): " + doubleToString(squash(f_selection, k_selection)));
  217. System.out.println(" w_selection * squash(f_selection, k_selection): " + doubleToString(w_selection * squash(f_selection, k_selection)));
  218. System.out.println("===================================================================");
  219. System.out.println(" f_holon: " + f_holon + ", k_holon: " + k_holon + ", w_holon: " + w_holon);
  220. System.out.println(" squash(f_holon, k_holon): " + doubleToString(squash(f_holon, k_holon)));
  221. System.out.println(" w_holon * squash(f_holon, k_holon): " + doubleToString(w_holon * squash(f_holon, k_holon)));
  222. System.out.println("===================================================================");
  223. System.out.println(" f_grid: " + f_grid + ", k_grid: " + k_grid + ", w_grid: " + w_grid);
  224. System.out.println(" squash(f_grid, k_grid): " + doubleToString(squash(f_grid, k_grid)));
  225. System.out.println(" w_grid * squash(f_grid, k_grid): " + doubleToString(w_grid * squash(f_grid, k_grid)));
  226. System.out.println("===================================================================");
  227. System.out.println();
  228. System.out.println();
  229. }
  230. static void printUnsquashedValues(double f_eb, double f_maximum, double f_holon, double f_selection, double f_grid){
  231. System.out.print(" f_eb(" + f_eb + ") ");
  232. System.out.print(" f_maximum(" + f_maximum + ") ");
  233. System.out.print(" f_holon(" + f_holon + ") ");
  234. System.out.print(" f_selection(" + f_selection + ") ");
  235. System.out.println(" f_grid(" + f_grid + ") ");
  236. }
  237. }