New Bio-inspired Meta-Heuristics-Green Herons Optimization Algorithm-for Optimization of Travelling Salesman Problem and Road Network

摘要

Following the nature and its processes has been proved to be very fruitful when it comes to tackling the difficult hardships and making life easy. Yet again the nature and its processes has been proven to be worthy of following, but this time the discrete family is being facilitated and another member is added to the bio-inspired computing family. A new biological phenomenon following meta-heuristics called Green Heron Optimization Algorithm (GHOA) is being introduced for the first time which acquired its potential and habit from an intelligent bird called Green Heron whose diligence, skills, perception analysis capability and procedure for food acquisition has overwhelmed many zoologists. This natural skillset of the bird has been transferred into operations which readily favor the graph based and discrete combinatorial optimization problems, both unconstrained and constraint though the latter requires safe guard and validation check so that the generated solutions are acceptable. With proper modifications and modeling it can also be utilized for other wide variety of real world problems and can even optimize benchmark equations. In this work we have mainly concentrated on the algorithm introduction with establishment, illustration with minute details of the steps and performance validation of the algorithm for a wide range of dimensions of the Travelling Salesman Problem combinatorial optimization problem datasets to clearly validate its scalability performance and also on a road network for optimized graph based path planning. The result of the simulation clearly stated its capability for combination generation through randomization and converging global optimization and thus has contributed another important member of the bio-inspired computation family.