Beam search is an informed algorithm for searching a graph that extends the Breadth-First search (Jungwirth, 2006). The Breadth-First search is essentially a special-case Best-First search where the evaluation function, f(n) is the same as the heuristics function, h(n) (JONES, 2008). Therefore in order to properly discuss Beam search, there is the need for a brief introduction to Best-First algorithm.
The Best-First search traverses a tree from top to bottom and searches for nodes on the same level first before descending to the next level. Nodes on the search frontier (nodes on the same level) are selected based on some evaluating function that is defined depending on the nature of the problem (heuristics function) and the estimated cost. The evaluating function f(n) is therefore the sum of the heuristic function h(n) and the estimated cost g(n). This algorithm is complete as it will always find the solution if it exists but it not optimal as it might find a solution of longer length depending on the heuristic function applied. The time and space complexity is both O(bm) where b is the branching factor and m is the tree depth.…
A COMPARITIVE STUDY OF NEAREST NEIGHBOUR ALGORITHM AND GENETIC ALGORITHM IN SOLVING TRAVELLING SALESMAN PROBLEM
Ajaz Ahmed Khan
Electronics and communication department
Mrs. Himani Agrawal
Electronics and communication department
Abstract—In this paper, we have used two algorithms, i.e. the Nearest Neighbor algorithm and Genetic Algorithm to solve the Travelling Salesman problem. The Travelling Salesman…
• PSO Algorithm to generate test cases
A. Initialize the population with N Particles where Program will search for optimal solution through the movement of these particles. And Set iterations counter I = 0.
B. Apply Fitness function: Calculating the fitness value by calculating the percentage of this particle will share in minimizing the total processing time to find the optimal solution.
C. Compare the calculated fitness value of each particle with its (lbest). If current value is better…
Divide and Conquer Strategies:
Divide and conquer is an algorithm which design paradigm based on multi-branched recursion. This designed paradigm consists of following phases:
1) Break the problem (divide): Breaking the problem into several sub-problems that are smaller in size.
2) Solve the sub problem(conquer) : Solve the sub-problem recursively .
3) Combine the solutions (Merge): Combine solutions to subproblems to create a solution to the original problem.
This technique is the basis of…
Travelling Salesman Problem
(using SA and GA)
Authors : IIT2015508, ITM2015006, IIT2015507, IIT2015131, IIT2015138
Under Guidance of : Dr. Vrijendra Singh
Abstract — TSP is an NP complete problem, presently there is no polynomial solution available. In this paper we try to solve this very hard problem using various heuristics such as Simulated Annealing, Genetic Algorithm to find a near optimal solution as fast as possible. We try to escape the local optimum,…
1) Linked Cluster Algorithm (LCA) , , :
LCA, was one of the initial clustering algorithms evolved. It was primarily developed for wired sensors, but later applied in wireless sensor networks. In LCA, a unique ID number is allotted to each node and has two means of becoming a cluster head. The first way is that if the node has the greatest ID number in the set including all neighbor nodes and the node itself. The second way is to assume that none of its neighbors are cluster heads,…
any other feature of intelligence can in principle be so precisely described
that a machine can be made to simulate it. (McCarthy, Minsky, Rochester &
Shannon, 1955, p. 13)
This idea of AI is the one we know today to be AGI. Unbeknownst to the researches at the time, the difficulty to implement the study was immense. This may have been due the attitude of others who felt developing AI didn’t coincide with knowledge of the human brain (Crevier, 1993, p. 275). From the lack of…
When I look at today 's world, it is hard to imagine another field that is going to change our society as much as computer technology does. I believe in the vision illustrated in Jeremy Rifkin 's book “The Zero Marginal Cost Society” that the emerging technology infrastructure – the Internet of Things (IoT) – will connect everyone and everything, use Big Data, analytics and algorithms to increase productivity and efficiency, and lower the marginal cost of producing and sharing to near zero. I…
After I realized the charm of developing my own software, making the cold code alive, I felt on top of the world. Although it is not flawless, our MOOC have basic functions and friendly user interface thanks to bootstrap and jquery, and the feedbacks given by our classmates are extremely high and positive.
Blended with the solid academic abilities and passion in coding and research, I further released my potential by involving in an innovation program which focused on reverse image search in…
Abstract—Feature selection, used as a preprocessing step, can reduce the dimensionality of data and thereby increase the efficiency, accuracy, and clarity of learning systems. However feature selection can be costly endeavour. This paper proposes two new feature selection algorithms, based on binary particle swarm optimisation, with the aim of reducing running time without affecting classification accuracy by combining filter and wrapper approaches. The first algorithm proceeds cautiously by…