Lecture 12 Heuristic Searches
- 1. Heuristic Search Algorithm Lecture-12 Hema Kashyap 1
- 2. Best First Search • Uniform Cost Search is a special case of the best first search algorithm. The algorithm maintains a priority queue of nodes to be explored. A cost function f(n) is applied to each node. The nodes are put in OPEN in the order of their f values. Nodes with smaller f(n) values are expanded earlier. The generic best first search algorithm is outlined below. 2
- 3. Concept Step 1: Traverse the root node Step 2: Traverse any neighbour of the root node, that is maintaining a least distance from the root node and insert them in ascending order into the queue. Step 3: Traverse any neighbour of neighbour of the root node, that is maintaining a least distance from the root node and insert them in ascending order into the queue Step 4: This process will continue until we are getting the goal node 3
- 4. Example Step 1: Consider the node A as our root node. So the first element of the queue is A whish is not our goal node, so remove it from the queue and find its neighbour that are to inserted in ascending order. A Step 2: The neighbours of A are B and C. They will be inserted into the queue in ascending order. B C A(Expanded Node) Step 3: Now B is on the FRONT end of the queue. So calculate the neighbours of B that are maintaining a least distance from the roof. F E D C B(Expanded Node) 4
- 5. Step 4: Now the node F is on the FRONT end of the queue. But as it has no further children, so remove it from the queue and proceed further. E D C F (Expanded Node) Step 5: Now E is the FRONT end. So the children of E are J and K. Insert them into the queue in ascending order. K J D C E(Expanded Node) Step 6: Now K is on the FRONT end and as it has no further children, so remove it and proceed further J D C K(Expanded Node) Step7: Also, J has no corresponding children. So remove it and proceed further. D C J(Expanded Node) Step 8: Now D is on the FRONT end and calculates the children of D and put it into the queue. I C D(Expanded Node) Step9: Now I is the FRONT node and it has no children. So proceed further after removing this node from the queue. C I(Expanded Node) 5
- 6. Step 10: Now C is the FRONT node .So calculate the neighbours of C that are to be inserted in ascending order into the queue. G H C(Expanded Node) Step 11: Now remove G from the queue and calculate its neighbour that is to insert in ascending order into the queue. M L H G(Expanded Node) Step12: Now M is the FRONT node of the queue which is our goal node. So stop here and exit. L H M(Expanded Node) 6
- 7. Advantages • It is more efficient than that of BFS and DFS. • Time complexity of Best first search is much less than Breadth first search. • The Best first search allows us to switch between paths by gaining the benefits of both breadth first and depth first search. Because, depth first is good because a solution can be found without computing all nodes and Breadth first search is good because it does not get trapped in dead ends. Disadvantages • Sometimes, it covers more distance than our consideration. 7
- 8. Branch and Bound • Branch and Bound is an algorithmic technique which finds the optimal solution by keeping the best solution found so far. • If partial solution can’t improve on the best it is abandoned, by this method the number of nodes which are explored can also be reduced. • It also deals with the optimization problems over a search that can be presented as the leaves of the search tree. • The usual technique for eliminating the sub trees from the search tree is called pruning. • For Branch and Bound algorithm we will use stack data structure. 8
- 9. Concept Step 1: Traverse the root node. Step 2: Traverse any neighbour of the root node that is maintaining least distance from the root node. Step 3: Traverse any neighbour of the neighbour of the root node that is maintaining least distance from the root node. Step 4: This process will continue until we are getting the goal node. 9
- 10. Algorithm Step 1: PUSH the root node into the stack. Step 2: If stack is empty, then stop and return failure. Step 3: If the top node of the stack is a goal node, then stop and return success. Step 4: Else POP the node from the stack. Process it and find all its successors. Find out the path containing all its successors as well as predecessors and then PUSH the successors which are belonging to the minimum or shortest path. Step 5: Go to step 5. Step 6: Exit. 10
- 11. ExampleStep 1: Consider the node A as our root node. Find its successors i.e. B, C, F. Calculate the distance from the root and PUSH them according to least distance. A B: 0+5 = 5 (The cost of A is 0 as it is the starting node) F: 0+9 = 9 C: 0+7 = 7 Here B (5) is the least distance. Step 2: Now the stack will be C F B(Top) A(Expanded) As B is on the top of the stack so calculate the neighbours of B. D: 0+5+4 = 9 E: 0+5+6 = 11 The least distance is D from B. So it will be on the top of the stack. 11
- 12. Step 3: As the top of the stack is D. So calculate neighbours of D. C F D(Top) B(Expanded) C: 0+5+4+8 = 17 F: 0+5+4+3 = 12 The least distance is F from D and it is our goal node. So stop and return success. Step 4: C F(Top) D(Expanded) Hence the searching path will be A-B -D-F 12
- 13. Advantages/Disadvantages Advantages: • As it finds the minimum path instead of finding the minimum successor so there should not be any repetition. • The time complexity is less compared to other algorithms. Disadvantages: • The load balancing aspects for Branch and Bound algorithm make it parallelization difficult. • The Branch and Bound algorithm is limited to small size network. In the problem of large networks, where the solution search space grows exponentially with the scale of the network, the approach becomes relatively prohibitive. 13