Mathematical Models of Nature Inspired Computational Intelligence for Terrain Understanding

Abstract

Nature inspired intelligence has emerged as a crucial means of implementing machine intelligence with human-like reasoning capabilities and an efficient mechanism for handling diverse uncertainty characteristics. These techniques can form the basis of building optimization algorithms which can adapt itself to suit the purpose of natural terrain understanding, and prove to be better by giving more accurate results than the other existing optimization techniques. The purpose of this research work is to develop new optimization models inspired from emerging geo-sciences techniques, extend the original optimization models of recent nature inspired techniques or build hybrid models of different nature inspired techniques by analyzing their performance governing factors, and hence present an adaptive framework for the terrain understanding problem. The research focuses on two major categorizations in the terrain understanding application: geo-spatial feature extraction, path planning and location prediction on remote sensing inputs. newlineThe research work starts by establishing the concept of information sharing in swarm intelligence techniques which is the key factor governing the heuristic function definition and leading towards optimal solutions. It establishes entropy and similarity index as the performance governing factors influencing the classification efficiency of biogeography based classifier. Based on this analysis, two new hybrid classifiers are developed, the first is an integration of BBO with ACO2/PSO and second, is an integration of BBO- GS with ACO2/PSO. These classifiers achieve very high kappa coefficients and prove to be great advancements over the existing classifiers. newlineThe research work moves ahead and focuses on the design of a new optimization algorithm BBO/EE and establishment of the factors of extinction and evolution for the development of extended model of species abundance in biogeography.