Nature Inspired Optimization For Real World Problems

Background: Nature Inspired Optimization techniques are meta-heuristic techniques which are inspired from nature’s laws and behavioral patterns. This field of study comes under artificial intelligence space search techniques which involve space explorations and exploitations to search for solutions while involving randomness in overall procedure. The pattern imitate some of the nature’s manner of evolving solutions through generation’s evolutions and iterative refinement. The philosophy to involve randomness in space searches just like nature do, enable these techniques to reach to near to optimal solutions even for NP hard problem domains. Therefore such techniques have seen a great applications in many computational optimization problems in complex real world domains. In past three decades many such algorithmic techniques have been developed by researchers. However, their modelling to suit specific application area seems an interesting and open area of research. The research work presented here is focused towards putting light on recent techniques, trends and application areas of these nature inspired meta-heuristic algorithms to some very interesting real world problems. The discussion in this paper identifies application of nature inspired optimization in primarily five main application area named health sector, social networks, transportation and education sector. The work presented will provide a consolidated reference for researchers for current state of the art in this area of research and guide for future research directions.