Conventional optimization methods were needed huge computational attempts, which grow exponentially as   difficulty dimension enhances with the networks of independent nodes. Researchers are continuously working upon the challenges like communication link failures, low memory, calculating constraints, and maximum valued energy. A lot of problems may formulated and approached through multidimensional optimization problems, in which most nodes are not neighbors of one another, but can be reached from every other by a small number of hops. Optimization problems under uncertainty are complex and difficult, and often classical algorithmic approaches based on mathematical and dynamic programming are able to solve only very small problem instances. For this reason, in the recent years meta-heuristic algorithm such as Ant Colony Optimization, Evolutionary Computation etc. are emerging as successful alternatives to classical approaches