TopologieObjectiveFunction.java 12 KB

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  1. package holeg.algorithm.objective_function;
  2. import java.util.Locale;
  3. import java.util.logging.Logger;
  4. import holeg.algorithm.objective_function.GraphMetrics.Graph;
  5. import holeg.ui.model.Model;
  6. import holeg.utility.math.decimal.Sampler;
  7. public class TopologieObjectiveFunction {
  8. private static final Logger log = Logger.getLogger(TopologieObjectiveFunction.class.getName());
  9. //Parameters
  10. //weight for f_g(H)
  11. static double w_eb = 0.2, w_max = 0.5, w_holon= 0.1, w_selection = .1, w_grid = 0.1;
  12. //--> f_eb parameter
  13. /**
  14. * Maximum Energie Difference(kappa)
  15. */
  16. static double k_eb = 5000.f;
  17. /**
  18. * Maximum when all on Energie Difference(kappa)
  19. */
  20. static double k_max = 10.f;
  21. static double lambda_max = 10.;
  22. //--> f_holon parameter
  23. /**
  24. * maximum penalty from holon element distribution
  25. */
  26. static double k_holon= 4000;
  27. //--> f_selection paramaeter;
  28. /**
  29. * average Maximum Cost for selction(kappa) of switch and elements.
  30. */
  31. static double k_selection = 200000;
  32. static double cost_switch = 3000;
  33. private static double cost_of_cable_per_meter = 6;
  34. //--> f_grid parameter
  35. /**
  36. * The avergae shortest path maximum length -> kappa for the squash function
  37. */
  38. static double k_avg_shortest_path = 1600;
  39. //Disjpijoint path cant have zero as output it starts with the value 1
  40. static double centerValue_disjoint_path = 1.0;
  41. static double k_disjoint_path = 2.4;
  42. static double lambda_avg_shortest_path = 10;
  43. static double lambda_disjoint_path = 10;
  44. static double k_grid = lambda_avg_shortest_path;// + lambda_disjoint_path;
  45. //pre-calculated parameters for partial function terms:
  46. /**
  47. * Pre calculated for the squash function
  48. * <br>
  49. * {@link TopologieObjectiveFunction#squash}
  50. */
  51. static double squash_subtract = 1.0f / (1.f + (float) Math.exp(5.0));
  52. static double range_for_k_avg_shortest_path = range(k_avg_shortest_path);
  53. static double range_for_k_disjoint_path = range(k_disjoint_path - centerValue_disjoint_path);
  54. public static Sampler averageLog = new Sampler();
  55. static boolean useLog = false;
  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 if more prints should be made
  80. * @return f_g value between 0 and 100
  81. */
  82. //TODO(Tom2022-01-13) Fix TopologyFitnessFunction
  83. static public float getFitnessValueForState(Model model, int amountOfAddedSwitch, double addedCableMeters, boolean moreInformation) {
  84. //
  85. //
  86. //
  87. // //Calculate f_eb the penalty for unbalenced energy in the network
  88. // double f_eb = 0;
  89. // for(DecoratedNetwork net : state.getNetworkList()) {
  90. // //abs
  91. // f_eb += Math.abs(net.getTotalConsumption() - net.getTotalProduction());
  92. // }
  93. // //Average?
  94. // f_eb /= state.getNetworkList().size();
  95. //
  96. //
  97. //
  98. //
  99. // double f_maximum = 0;
  100. // for(DecoratedNetwork net : state.getNetworkList()) {
  101. // double prod = net.getTotalProduction();
  102. // double con = net.getTotalConsumption();
  103. // if(prod == 0 || con == 0) {
  104. // f_maximum += lambda_max;
  105. // }else {
  106. // f_maximum += lambda_max * (Math.abs(prod - con)/Math.max(prod, con));
  107. // }
  108. // }
  109. // //Average?
  110. // f_maximum /= state.getNetworkList().size();
  111. //
  112. // //calculate f_holon
  113. // double f_holon = 0;
  114. // for(DecoratedNetwork net : state.getNetworkList()) {
  115. // double f_elements_deviation_production = net.getDeviationInProductionInNetworkForHolonObjects();
  116. // double f_elements_deviation_consumption = net.getDeviationInConsumptionInNetworkForHolonObjects();
  117. // double f_element = f_elements_deviation_production+f_elements_deviation_consumption;
  118. // f_holon += f_element;
  119. // }
  120. // f_holon /= state.getNetworkList().size();
  121. //
  122. // //calculating f_selection
  123. // double f_selection = calculateTopologieCost(state, amountOfAddedSwitch, addedCableMeters);
  124. // //if(moreInformation)LOGGER.info("CostForWildcards:" + cost + ", CostSwitches(#" + amountOfAddedSwitch +"):" + cost_switch * amountOfAddedSwitch + ", CostCables(" +addedCableMeters+ "m):" + cost_of_cable_per_meter * addedCableMeters);
  125. //
  126. //
  127. // //calculating f_grid
  128. // double f_grid = 0;
  129. // //each network is a holon
  130. // for(DecoratedNetwork net: state.getNetworkList()) {
  131. // Graph G = GraphMetrics.convertDecoratedNetworkToGraph(net);
  132. // //We have to penalize single Networks;
  133. // if(G.V.length <= 1 || G.S.length <= 1) {
  134. // f_grid += lambda_avg_shortest_path;// + lambda_disjoint_path;
  135. // continue;
  136. // }
  137. //
  138. // double avgShortestPath = GraphMetrics.averageShortestDistance(G);
  139. // //double disjpointPaths = GraphMetrics.averageEdgeDisjointPathProblem(G);
  140. // if(useLog) {
  141. // averageLog.addSample("avgShortestPath", (float)avgShortestPath);
  142. // }
  143. // f_grid += avgShortestPathPenalty(avgShortestPath);// + disjoinPathPenalty(disjpointPaths);
  144. // }
  145. // //take average to encourage splitting
  146. // f_grid /= state.getNetworkList().size();
  147. //
  148. //
  149. //
  150. //
  151. // if(moreInformation) {
  152. // printWeightedValues(f_eb, f_maximum, f_holon, f_selection, f_grid);
  153. // if(useLog) {
  154. // log.info(averageLog.toString());
  155. // }
  156. // }
  157. // //printUnsquashedValues(f_eb, f_maximum, f_holon, f_selection, f_grid);
  158. // if(useLog) {
  159. // averageLog.addSample("Unsquashed f_eb", (float)f_eb);
  160. // averageLog.addSample("Unsquashed f_maximum", (float)f_maximum);
  161. // averageLog.addSample("Unsquashed f_holon", (float)f_holon);
  162. // averageLog.addSample("Unsquashed f_selection", (float)f_selection);
  163. // averageLog.addSample("Unsquashed f_grid", (float)f_grid);
  164. // }
  165. // return (float) (w_eb * squash(f_eb, k_eb)
  166. // + w_max * squash(f_maximum, k_max)
  167. // + w_holon * squash(f_holon, k_holon)
  168. // + w_selection * squash(f_selection, k_selection)
  169. // + w_grid * squash(f_grid, k_grid));
  170. return 0;
  171. }
  172. //
  173. // public static double calculateTopologieCost(DecoratedState state, int amountOfAddedSwitch,
  174. // double addedCableMeters) {
  175. // double cost = calculateWildcardCost(state);
  176. // cost += calculateAddedSwitchCost(amountOfAddedSwitch);
  177. // cost += calculateAddedCableCost(addedCableMeters);
  178. // return cost;
  179. // }
  180. //
  181. // public static double calculateAddedCableCost(double addedCableMeters) {
  182. // return cost_of_cable_per_meter * addedCableMeters;
  183. // }
  184. //
  185. // public static double calculateAddedSwitchCost(int amountOfAddedSwitch) {
  186. // return cost_switch * amountOfAddedSwitch;
  187. // }
  188. //
  189. // public static double calculateWildcardCost(DecoratedState state) {
  190. // double cost = 0;
  191. // for(DecoratedNetwork net : state.getNetworkList()) {
  192. // for(DecoratedHolonObject dHobject : net.getConsumerList()) {
  193. // if(dHobject.getModel().getName().contains("Wildcard")){
  194. // if(dHobject.getModel().getName().length() > 9) {
  195. // String costString = dHobject.getModel().getName().substring(9);
  196. // cost += Double.parseDouble(costString);
  197. // }
  198. // }
  199. // }
  200. // for(DecoratedHolonObject dHobject : net.getConsumerSelfSuppliedList()) {
  201. // if(dHobject.getModel().getName().contains("Wildcard")){
  202. // if(dHobject.getModel().getName().length() > 9) {
  203. // String costString = dHobject.getModel().getName().substring(9);
  204. // cost += Double.parseDouble(costString);
  205. // }
  206. // }
  207. // }
  208. // for(DecoratedHolonObject dHobject : net.getSupplierList()) {
  209. // if(dHobject.getModel().getName().contains("Wildcard")){
  210. // if(dHobject.getModel().getName().length() > 9) {
  211. // String costString = dHobject.getModel().getName().substring(9);
  212. // cost += Double.parseDouble(costString);
  213. // }
  214. // }
  215. // }
  216. // }
  217. // return cost;
  218. // }
  219. private static String doubleToString(double value) {
  220. return String.format (Locale.US, "%.2f", value);
  221. }
  222. @SuppressWarnings("unused")
  223. private static double disjoinPathPenalty(double value) {
  224. return -(2.0 * lambda_disjoint_path) / (1 + Math.exp(- (value - centerValue_disjoint_path)/ range_for_k_disjoint_path)) + (2.0 * lambda_disjoint_path);
  225. }
  226. private static double avgShortestPathPenalty(double value) {
  227. return (2.0 * lambda_avg_shortest_path) / (1 + Math.exp(- value/ range_for_k_avg_shortest_path)) - lambda_avg_shortest_path;
  228. }
  229. /**
  230. * Attention Math.log calcultae ln not log
  231. * @param kappa
  232. * @return
  233. */
  234. private static double range(double kappa) {
  235. return - kappa / Math.log(Math.pow(2.0, 0.05) - 1.0 );
  236. }
  237. /**
  238. * The squashing function in paper
  239. * @param x the input
  240. * @param kappa the corresponding kappa
  241. * @return
  242. */
  243. static public double squash(double x, double kappa) {
  244. return 100.f/(1.0f + Math.exp(-(10.f * (x - kappa/2.f))/ kappa)) - squash_subtract;
  245. }
  246. /**
  247. * f_sup in paper
  248. * @param supply from 0 to 1
  249. * @return
  250. */
  251. static public double supplyPenalty(double supply) {
  252. double supplyPercentage = 100 * supply;
  253. return (supplyPercentage < 100) ? -0.5 * supplyPercentage + 50: supplyPercentage - 100;
  254. }
  255. static void printWeightedValues(double f_eb, double f_maximum, double f_holon, double f_selection, double f_grid){
  256. log.info("===================================================================");
  257. log.info(" f_eb: " + f_eb + ", k_eb: " + k_eb + ", w_eb: " + w_eb);
  258. log.info(" squash(f_eb, k_eb): " + doubleToString(squash(f_eb, k_eb)));
  259. log.info(" w_eb * squash(f_eb, k_eb): " + doubleToString(w_eb * squash(f_eb, k_eb)));
  260. log.info("===================================================================");
  261. log.info(" f_maximum: " + f_maximum + ", k_max: " + k_max + ", w_max: " + w_max);
  262. log.info(" squash(f_maximum, k_max): " + doubleToString(squash(f_maximum, k_max)));
  263. log.info(" w_max * squash(f_maximum, k_max): " + doubleToString(w_max * squash(f_maximum, k_max)));
  264. log.info("===================================================================");
  265. log.info(" f_selection: " + f_selection + ", k_selection: " + k_selection + ", w_selection: " + w_selection);
  266. log.info(" squash(f_selection, k_selection): " + doubleToString(squash(f_selection, k_selection)));
  267. log.info(" w_selection * squash(f_selection, k_selection): " + doubleToString(w_selection * squash(f_selection, k_selection)));
  268. log.info("===================================================================");
  269. log.info(" f_holon: " + f_holon + ", k_holon: " + k_holon + ", w_holon: " + w_holon);
  270. log.info(" squash(f_holon, k_holon): " + doubleToString(squash(f_holon, k_holon)));
  271. log.info(" w_holon * squash(f_holon, k_holon): " + doubleToString(w_holon * squash(f_holon, k_holon)));
  272. log.info("===================================================================");
  273. log.info(" f_grid: " + f_grid + ", k_grid: " + k_grid + ", w_grid: " + w_grid);
  274. log.info(" squash(f_grid, k_grid): " + doubleToString(squash(f_grid, k_grid)));
  275. log.info(" w_grid * squash(f_grid, k_grid): " + doubleToString(w_grid * squash(f_grid, k_grid)));
  276. log.info("===================================================================");
  277. }
  278. static void printUnsquashedValues(double f_eb, double f_maximum, double f_holon, double f_selection, double f_grid){
  279. System.out.print(" f_eb(" + f_eb + ") ");
  280. System.out.print(" f_maximum(" + f_maximum + ") ");
  281. System.out.print(" f_holon(" + f_holon + ") ");
  282. System.out.print(" f_selection(" + f_selection + ") ");
  283. log.info(" f_grid(" + f_grid + ") ");
  284. }
  285. }