Multi Neuron Heuristic Search

We know the various searching algorithms available today. Searching has become one of the most essential parts of the artificial intelligence algorithms these days. We have so many algorithms like A*, Heuristic Search, Breadth-First Search, Depth First Search, etc. All these are applied to various problems in their own way. We need to predict the most appropriate search technique as the input data is not known. In this paper we present a new searching algorithm. This algorithm works on the principle of applying many neurons (elementary searching units) for working on different data one after the other. Hence as in the case of A * and heuristic search, we do not only select the best current node, but we select a range of nodes from the best to worst. At each iteration various nodes are seen and expanded which have varying heuristic costs. This algorithm would work very well on data in which heuristics change suddenly from very good to bad or viceversa. We implemented this algorithm and put it on the maze-solving problem, where the heuristic cost was the distance between the nodes to goal point. We saw that the algorithm worked better than any existing algorithm and visited the least number of nodes. This proves the efficiency of the algorithm. We have also shown that this algorithm lies between A * Algorithm and Breadth First Search. Both these algorithms can be reached using this algorithm.