  Many real world problems are NP-Hard problems are a very large part of them
can be represented as graph based problems. This makes graph theory a very
important and prevalent field of study. In this work a new bio-inspired
meta-heuristics called Green Heron Swarm Optimization (GHOSA) Algorithm is
being introduced which is inspired by the fishing skills of the bird. The
algorithm basically suited for graph based problems like combinatorial
optimization etc. However introduction of an adaptive mathematical variation
operator called Location Based Neighbour Influenced Variation (LBNIV) makes it
suitable for high dimensional continuous domain problems. The new algorithm is
being operated on the traditional benchmark equations and the results are
compared with Genetic Algorithm and Particle Swarm Optimization. The algorithm
is also operated on Travelling Salesman Problem, Quadratic Assignment Problem,
Knapsack Problem dataset. The procedure to operate the algorithm on the
Resource Constraint Shortest Path and road network optimization is also
discussed. The results clearly demarcates the GHOSA algorithm as an efficient
algorithm specially considering that the number of algorithms for the discrete
optimization is very low and robust and more explorative algorithm is required
in this age of social networking and mostly graph based problem scenarios.
