Green Heron Swarm Optimization Algorithm - State-of-the-Art of a New Nature Inspired Discrete Meta-Heuristics
This addresses the need for more efficient and robust algorithms in graph-based optimization, which is crucial for applications like social networking, though it appears incremental as it builds on existing meta-heuristic concepts.
The authors introduced the Green Heron Swarm Optimization (GHOSA) algorithm, a new bio-inspired meta-heuristic for discrete optimization problems like the Travelling Salesman Problem, and showed it outperforms Genetic Algorithm and Particle Swarm Optimization on benchmark tests.
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.